PERFORMANCE INDICATORS FOR
SMART GRIDS
An analysis of indicators that measure and evaluate smart grids
MARCUS TJÄDER
ISHAK BUSULADZIC
School of Business, Society and Engineering
Course: Degree project - energy engineering Course code: ERA401
Credits: 30 hp
Program: M.Sc. in Sustainable Energy Systems
Supervisor: Erik Dahlquist, Mälardalen University;
Johanna Rosenlind, Swedish Energy Markets Inspectorate
Examinor: Jan Sandberg
Costumer: Swedish Energy Markets Inspectorate Date: 2020-05-28
Email:
ABSTRACT
Sweden has developed ambitious goals regarding energy and climate politics. One major goal is to change the entire electricity production from fossil fuels to sustainable energy sources, this will contribute to Sweden being one of the first countries in the world with non-fossil fuel in the electricity sector. To manage this, major changes need to be implemented and
difficulties on the existing grid will occur with the expansion of digitalization, electrification and urbanization. By using smart grids, it is possible to deal with these problems and change the existing electricity grid to use more distributed power generation, contributing to
flexibility, stability and controllability. The goal with smart grids is to have a sustainable electricity grid with low losses, security of supply, environmental-friendly generation and also have choices and affordable electricity for customers. The purpose of this project is to identify and evaluate several indicators for a smart grid, how they relate and are affected when
different scenarios with different technologies are implemented in a test system. Smart grid indicators are quantified metrics that measure the smartness of an electrical grid. There are five scenarios where all are based on possible changes in the society and electricity
consumption, these scenarios are; Scenario A – Solar power integration, Scenario B – Energy storage integration, Scenario C – Electric vehicles integration, Scenario D – Demand
response and Scenario E – Solar power, Energy storage, Electric vehicles and Demand
response integration. A model is implemented in MATLAB and with Monte Carlo simulations expected values, standard deviation and confidence interval were gained. Four selected indicators (Efficiency, capacity factor, load factor and relative utilization) was then analyzed. The results show that progress on indicators related to all smart grid characteristics is needed for the successful development of a smart grid. In scenario C, all four selected indicators improved. This shows that these indicators could be useful for promoting the integration of electric vehicles in an electricity grid. In Scenario A, solar power integration contributed to all indicators deteriorate, this means that, technical solutions that can stabilize the grid are necessary to implement when integrating photovoltaic systems. The load factor is a good indicator for evaluating smart grids. This indicator can incentivize for an even load and minimize the peak loads which contributes to a flexible and efficient grid. With the capacity factor, the utilization and free capacity can be measured in the grid, but it can counteract renewable energy integration if the indicator is used in regulation.
Keywords: Performance Indicators, Swedish Energy Markets Inspectorate, Electric Vehicle,
Photovoltaic, Energy storage, Distributed electricity grid, Demand response, Smart grid, Monte-Carlo simulation, Renewable Energy
PREFACE
This project is performed for the degree of Master of Science in Engineering at Mälardalen University in Västerås, Sweden. The project has been supervised by Erik Dahlquist from January 2020 to June 2020 in the department of Business, Society and Engineering.
This degree project is done on behalf of the Swedish Energy Markets Inspectorate, an expert and regulatory authority with the mission to work for well-functioning energy markets in Sweden. The electricity grids need to be changed and modernized to meet the change of energy system; therefore, performance indicators are of importance for the authority. Together with the Swedish Energy Markets Inspectorate the research questions and the purpose were developed and formulated.
We want to thank the employees at the Swedish Energy Markets Inspectorate that was involved in this degree project. Especially thanks to our supervisors Johanna Rosenlind and Maria Dalheim who had assisted us through the whole project with professionalism, positive motivation, and important tips.
Gratitude to Erik Dahlquist at Mälardalen University for pleasant and rewarding meetings throughout the project period and, also other participates who shared important information.
Västerås, June 2020
CONTENT
1. INTRODUCTION ... 1
1.1. Swedish energy markets inspectorate ... 1
1.2. Background ... 2 1.3. Purpose ... 3 1.4. Research questions ... 3 1.5. Delimitation... 4 2. METHOD ... 5 3. LITERATURE STUDY... 7
3.1. Swedish electrical grid ... 7
3.1.1. Regulation of the Swedish electricity grid ... 8
3.2. Smart grids ... 10
3.2.1. Distributed power generation ... 13
3.2.2. Technologies in smart grids ... 15
3.2.3. Smart meters ... 16
3.2.4. Energy storage ... 18
3.2.5. Demand response ... 19
3.3. Performance indicators... 20
3.3.1. Hosting capacity ... 21
3.3.2. Maximum power injection ... 22
3.3.3. Losses in transmission and distribution ... 22
3.3.4. Voltage variations... 23
3.3.5. Capacity factor ... 24
3.3.6. Resiliency ... 24
3.3.7. Efficiency ... 25
3.3.8. Utilization of electricity grid ... 26
3.3.8.1. ACTUAL UTILIZATION ... 26
3.3.8.2. RELATIVE UTILIZATION... 26
3.3.9. Load factor ... 27
3.3.11. Energy not withdrawn from renewable sources due to congestion or security
risks ... 28
3.3.12. Smart meters ... 28
3.3.13. Share of renewable energy ... 29
3.3.14. Analysis of the performance indicators... 29
3.4. Monte Carlo simulations ... 31
3.4.1. Estimate ... 32
3.4.2. Confidence interval ... 33
3.4.3. Expected value, Variance & Standard deviation ... 33
4. CURRENT STUDY ... 34 4.1. Simulation ... 34 4.1.1. Simulation limitations ... 35 4.1.2. Test system ... 36 4.1.3. Load curves ... 37 4.1.4. Temperature ... 39
4.1.5. Analyzed indicators in the simulations... 40
4.2. Scenarios ... 41
4.2.1. Scenario A: Solar Power integration... 41
4.2.1.1. SOLAR SYSTEM CALCULATIONS ... 43
4.2.2. Scenario B: Energy Storage Integration ... 44
4.2.2.1. BATTERY CALCULATIONS ... 44
4.2.3. Scenario C: Electric vehicles integration ... 45
4.2.4. Scenario D: Demand response... 46
4.2.5. Scenario E: PV, Energy storage, EV: s and Demand response ... 47
5. RESULTS ... 48
5.1. Baseline scenario ... 48
5.1.1. Baseline scenario N = 10 000 ... 49
5.2. Scenario A: Solar power integration ... 49
5.3. Scenario B: Energy storage integration... 52
5.4. Scenario C: Electric vehicle integration... 54
5.6. Scenario E: PV, Energy storage, EV: s and Demand response ... 58
5.7. Analyzed result of all scenarios including the different portions of integration 61 6. DISCUSSION ... 64 6.1. Smart grids ... 64 6.2. Test system ... 64 6.3. Indicator analysis... 64 6.4. Limitations ... 65 6.5. Temperature ... 66 6.6. Scenarios ... 67 7. CONCLUSIONS ... 68
8. SUGGESTIONS FOR FURTHER WORK ... 69
LIST OF FIGURES
Figure 1 Flow chart for the methodology of the project ... 6 Figure 2 A schematic overview of the Swedish electricity system with different actors in the
system and with three voltage levels ... 8 Figure 3 The regulatory model used by the Swedish energy market inspectorate in the
pre-regulation process of electric grid companies’ revenue. ... 10 Figure 4 Visualization of a smart grid ...13 Figure 5 Centralized and distributed power generation are presented where the distributed
power generation contains more variations of energy sources...14 Figure 6 Traditional electricity meter compared with a smart meter, presenting how different functionalities utilizing the two way-communication... 17 Figure 7 Hosting capacity approach where the performance indicator deteriorates (Inspired
by Bollen & Rönnberg, 2017) ...21 Figure 8 Flowchart for the used algorithm in the simulation ... 35 Figure 9 Test system that represent a typical Swedish urban distribution network (Engblom
& Ueda, 2008) (Permission granted) ... 36 Figure 10 Categorization of the load curves ... 38 Figure 11 Example on load curves for household, industry and the commercial sector for the
temperature interval +5 °C to +10 °C ... 39 Figure 12 Hourly PV production simulated for a year using Opti-CE in MATLAB ... 42 Figure 13 Load curve for EV:s charged at home, assuming 25 % of households have an EV .. 46 Figure 14 The load curve with PV integration (Scenario A) compared to baseline scenario for
January 31st (weekday). ... 51
Figure 15 The load curve with PV integration (Scenario A) compared to baseline scenario for June 31st (weekend). ... 51
Figure 16 The load curve with battery integration compared to baseline scenario for January 31st. (weekday). ... 53
Figure 17 The load curve with energy storage integration (Scenario B) compared to baseline scenario for June 31st (weekend). ... 53
Figure 18 The load curve with EV integration (Scenario C) compared to baseline scenario for January 31st (weekday). ... 55
Figure 19 The load curve with electrical vehicle integration (Scenario C) compared to baseline scenario for June 31st (weekend). ... 55
Figure 20 The load curve with demand response integration (Scenario D) compared to baseline scenario for January 31st (weekday)... 57
Figure 21 The load curve with demand response (Scenario D) compared to baseline scenario for June 31st (weekend). ... 57
Figure 22 The load curve with demand response (Scenario D) compared to baseline scenario for January 31st (weekend). ... 58
Figure 23 The load curve with PV, battery, EV and demand response (Scenario E) compared to baseline scenario for January 31st (weekday). ... 59
Figure 24 The load curve with PV, battery, EV and demand response (Scenario E) compared to baseline scenario for June 31st (weekend). ... 60
LIST OF TABLES
Table 1 Differences between smart grids and traditional grids ...12
Table 2 Interruption indicators... 25
Table 3 Summary Indicators ... 29
Table 4 Yearly average consumption [MWh] and yearly average power [kW] per customer . 37 Table 5 Mean temperature with the standard deviation for every month ... 40
Table 6 The estimated values of Expected value, Standard deviation and 95 % confidence interval for each indicator in baseline scenario ... 48
Table 7 The estimated values of the Expected value, Standard deviation and 95 % Confidence interval for each indicator in the baseline scenario using N = 10 000 iteration. This result is used to demostrate the sufficiency of 1500 iterations in the remaning simulations ... 49
Table 8 The estimated values of Expected value, Standard deviation and 95 % Confidence interval for each indicator in Scenario A – the solar power integration scenario .. 50
Table 9 The estimated values of Expected value, Standard deviation and 95 % Confidence interval for each indicator in Scenario B – the energy storage integration scenario ... 52
Table 10 The estimated values of Expected value, Standard deviation and 95 % Confidence interval for each indicator in Scenario C – the electric vehicle integration scenario ... 54
Table 11 The estimated values of Expected value, Standard deviation and 95 % Confidence interval for each indicator in Scenario D – the demand response scenario ... 56
Table 12 The estimated values of Expected value, Standard deviation and 95 % Confidence interval for each indicator in Scenario E – the all technologies integration scenario ... 58
Table 13 Summary table for all scenarios and with different portions of technology integration ...61
LIST OF EQUATIONS
Losses in transmission and distribution ... 23Capacity factor... 24 SAIDI…….. ... 25 SAIFI…….. ... 25 CAIDI……... 25 MAIFI………. ... 25 Efficiency… ... 25 Actual utilization ... 26 Relative utilization ... 26 Load factor ... 27
Operational ratio ... 27
Share of renewable energy ... 29
Confidence interval ... 33
𝑆𝑜𝑙𝑎𝑟 𝑟𝑎𝑑𝑖𝑎𝑡𝑖𝑜𝑛 ... 43
Global tilted radiation ... 43
Diffuse radiation... 43
Reflected radiation ... 43
Hourly power output ... 43
State of charge (charging) ... 44
State of charge (discharging) ... 45
Load profile EV... 45
NOMENCLATURE
Symbol Description Unit
C Capacity MW
D(X) Standard deviation -
E Energy MWh
E(X) Expected value -
G Radiation W/m2 𝐼𝑐𝑓 Capacity factor % 𝐼𝑒𝑓𝑓 Efficiency MW/MW 𝐼𝑙𝑓 Load factor MW/MW 𝐼𝑙𝑓 Relative Utilization MW/MW N Numbers of iterations - OR Operational Ratio - P Power MW P(X) Probability - T Temperature C t Time Hours V(X) Variance - Confidence level - Tilt angel Solar altitude
Efficiency %
θ Parameter -
Output temperature coefficient %/C
Reflectance from the ground -
Self-discharge %
υ Wind speed m/s
Angle of incidence
ABBREVIATIONS
Abbreviation DescriptionAIF Average Interruption Frequency
AIT Average Interruption Time
AMI Advanced Metering Infrastructure
BEV Battery Electric Vehicle
CAIDI Costumer Average Interruption Duration Index
CAPEX Capital Expenditure
CEER Council of European Energy Regulators
CG Centralized Power Generation
CIGRE International Council on Large Electric Systems
DG Distributed Power Generation
DSO Distribution System Operator
EEA European Environment Agency
Ei Swedish Energy Markets Inspectorate
EU European Union
EV Electrical Vehicle
FACTS Flexible Alternating Current Transmission Systems Fstn Interconnection point for the distribution network
GHG Greenhouse Gases
HVDC High Voltage Direct Current
IEA International Energy Agency
KPI Key Performance Indicator
NOCT Nominal Operational Cell Temperature
Nstn Substation
OPEX Operating Expenditure
PHEV Plug-in Hybrid Electric Vehicle
PV Photo-Voltaic
RES Renewable Energy Source
SAIDI System Average Interruption Duration Index SAIFI System Average Interruption Frequency Index
SGAM Smart Grid Architecture Model
SMHI Swedish Meteorological and Hydrological Institute
SOC State of Charge
SORE Share of Renewable Energy
SvK Svenska Kraftnät
TSO Transmission System Operator
V2G Vehicle to Grid
WAMC Wide-Area Monitoring and Control
WAMS Wide-Area Measurement Systems
DEFINITIONS
Definition Description
MATLAB A programing language intended primarily for numerical computing.
Opti-CE Open source code for simulation of hybrid power systems. Internet of
1.
INTRODUCTION
In the coming 20 years, Sweden has set ambitious goals for energy and climate politics. The goals are about transforming the entire electricity production from fossil fuels to renewable energy sources and to be one of the first fossil-fuel-free countries in the world. These goals will affect the existing electricity grid in the way of new demand patterns such as increased electrification, digitalization, and urbanization. One way to approach this is to utilize the concept of smart grids, which would allow for today’s centralized energy system to integrate more fluctuating renewable energy sources and with digital technology make the system more flexible, robust and more controllable. (Swedishsmartgrid, 2019)
This report presents a degree project at Swedish energy markets inspectorate. The project aims to identify and evaluate smart grid indicators for a smart grid, with the integration of specific technologies; energy storage, electrical vehicles (EV:s), photovoltaic (PV) and demand response. Smart grid indicators are quantified metrics that measures the smartness of an electricity grid. An appropriateness assessment will be performed on indicators with respect to the grid development plan mentioned in the electricity market directive. The indicators will be analyzed in this project for the purpose of being used by the Swedish energy markets inspectorate to follow the development of a smart grids, which data from electricity grid operators, which can promote smart grid development.
1.1.
Swedish energy markets inspectorate
The Swedish energy markets inspectorate (Ei) is a regulatory authority which is working on behalf of the Swedish government and belongs to the ministry of infrastructure. Ei:s activities contribute to the implementation of the government and parliament’s policy for
well-functioning energy markets and the development of electricity and natural gas market in the European Union (EU) and Scandinavia. Also, with the expertise from the authority, Ei proposes legislative changes and other measures to develop the energy markets in Sweden. (Ei, 2019)
Ei has the mission to ensure that grid companies meet the electricity requirements and that the grids are reliable and efficient. Further on, the grid should in the long term fulfil
reasonable requirements for the transmission of electricity. Ei should ensure that the main market actors follow the laws within the energy market. (Ei, 2019a) The primary laws are:
• Electricity act (1997:857) • Natural gas act (2005:403) • District heating act (2008:263)
• The act on intervention against market abuse in the trading of wholesale energy products (2013:385)
• The act on certain pipelines and associated regulations and regulations (1978:160) Ei monitors the reliability of the grid, with grid companies reporting information about interruption data, grid losses, and load to the authority. This information is used as basis for supervision and to create incentives in the regulation of the companies’ revenue cap.
Interruption data from an electricity grid are important for the evaluation about the quality in grid operation, lower quality will lower the revenue cap while high quality results in an increase. The incentive for efficient use of the grid is currently based on two parts, the first part gives the incentives to reduce the grid losses and the second part gives the incentives for balancing the load. (Ei, 2019a)
1.2.
Background
The Swedish energy system needs development in order to readjust to renewable energy technologies that are seen as a primary solution to global challenges: climate change, energy security, and economic growth. Trends that both challenge and create possibilities for the energy system are, increased proportion of weather-dependent renewables, the striving for more energy efficiency, small-scale distributed electricity production, energy storage, new electric grid technologies, electrification of vehicles and digitization. These trends contribute to electricity as an energy carrier to become more important. (International Energy Agency [IEA], 2011)
To manage the transition of the energy system efficiently, the electric grid needs to be changed and modernized. This development of the electric grid is often referred to as smart grid implementation. The concept of smart grids consists of technical and administrative solutions that allow for more flexible use of the electric grid. From the customer perspective, one benefit with smart grids is enabling informed participation in the electricity market. This could be a customer making active choices about its consumption and different purchasing patterns. From a societal perspective, smart grids imply to use the energy efficiently and avoid using energy that could harm the climate. The main goal with smart grids is to
efficiently use the current electric grid and to form a long-term sustainable development for the electric grid. (IEA, 2011)
According to Næss-Schmidt et al. (2017) to participate in the energy transition, it is important to create incentives for the electric grid companies adapting their model and electric grid to meet the challenges in the transition to smart grids. They also mention that it is important to monitor that grid companies do not prevent the progress of smart grids, and that they are willing to adapt if it is needed for the development.
A step towards smart grids is the EU directive 2019/944 that aims to improve the EU regulatory framework for the internal electricity market. The directive 2019/944 cause (83), “Regulatory authorities should ensure that transmission system operators and distribution system operators take appropriate measures to make their network more resilient and flexible. To that end, they should monitor those operators' performance based on indicators such as the capability of transmission system operators and distribution system operators
to operate lines under dynamic line rating, the development of remote monitoring and real-time control of substations, the reduction of grid losses and the frequency and duration of power interruptions”. In addition, according to the directive, EU member states should promote more energy efficiency. The directive 2019/944 cause (51), “Member States should encourage the modernisation of distribution networks, such as through the introduction of smart grids, which should be built in a way that encourage decentralised generation and energy efficiency”. Article (59.1.l) mentions that in addition to energy efficiency, a smart grid should promote the integration of renewable energy sources and that indicators should be developed for further evaluation in order to follow up the smart grid development. The Council of European Energy Regulators use the following definition for smart grids: “A Smart Grid is an electricity network that can cost efficiently integrate the behaviour and actions of all users connected to it – generators, consumers and those that do both – in order to ensure economically efficient, sustainable power system with low losses and high levels of quality and security of supply and safety.”. (CEER,2014)
Incentives today use indicators to follow the electricity act requirements on efficient use of the electric grid. Companies’ revenue cap should, according to the electricity act, be
determined by to which extent the electric grid is used efficiently. (Ei, 2015) In the future it is important to follow the development of smart grids. Indicators are identified in order to, in an objective way, measure how good the electric grid companies develop their electric grid in a smart grid perspective. This will help with the supervision of the electric grid and create more possibilities to develop incentives in the regulation of the electricity grid companies’ revenue, which can be used to develop smart grids. (Ei, 2019a)
1.3.
Purpose
The purpose with this project is to identify and evaluate performance indicators that can be used to measure and evaluate smart grids.
1.4.
Research questions
• Which criteria should be used to estimate if the electric grid is smart? o What characterizes a smart grid?
o What is the goal with smart grids?
• Which indicators are most suitable for assessing the smart grid?
• Are there specific indicators that describe the development of certain smart grid characteristics?
1.5.
Delimitation
The project is delimited to study performance indicators that can evaluate smart grids in a technical aspect. The analysis focuses on the load at the interconnection point between the distributed network and the overlying network.
2.
METHOD
To get an overview of the concept and technologies in smart grids a literature study was conducted. The literature study focuses on what characterizes a smart grid and how it can be evaluated, the possible indicators are identified and then investigated. The literature study comes from scientific papers, reports, and books. The material was found by searching for keywords relating to the subject of smart grids, e.g. “Smart grid”, “Demand response”, “Smart grid indicators”, “Renewable energy” and “Monte Carlo simulations”. Searches were mostly made on Mälardalen University’s library-database, but also on authority and industry pages that are relevant.
An appropriateness assessment was performed on indicators that can be used for smart grids. The indicators are evaluated with the perspective, of being able to follow the
development of a smart grid, which then can promote energy efficiency, flexibility, reliability, and integration of renewable energy for electric grids.
In order to analyze the indicators, different scenarios were developed, which are based on possible changes in the society and electricity consumption. The analyzed scenarios are, solar power integration, energy storage integration, electric vehicle integration, and demand response. The indicators are analyzed by examining in what way they measure the electricity grid and relate to each other. A stochastic simulation model that uses Monte-Carlo
simulation was used for analysis.
The model that was used consists of a test system representing a Swedish power distribution network. The test system includes technical and customer properties that was developed in a previous study by Engblom & Ueda (2018) analyzing reliability in an electricity grid. The inputs in the model are the network structure, customer composition, electric load, and temperature data. From the Monte Carlo simulation, the output data for the indicators are expected values, the standard deviation and the confidence interval. Four selected indicators (Efficiency, capacity factor, load factor and relative utilization) was used in the simulation. The model is implemented in MATLAB and simulated, where relevant results was gained, and the evaluation of the four selected indicators could be performed. MATLAB was chosen as a suitable tool for the simulation because it is optimal for mathematical and technical calculations. Also, previous knowledge about MATLAB contributed to an organized development of the model.
Figure 1 (below), presents the process of the methodology.
3.
LITERATURE STUDY
In this chapter, the main aspects of the Swedish electrical grid are explained and a brief description of the regulation in the Swedish electricity market is described. Furthermore, a definition of smart grids and the goal with the implementation of smart grids is presented. Performance indicators found in the literature review are presented and their relation to smart grid evaluation is explained.
3.1.
Swedish electrical grid
The Swedish electricity grid is divided into three different levels; the national grid (transmission grid), the regional grid (regional distribution grid) and the local grid
(distribution grid) where the operating voltage governs the classification. The transmission grid has its lowest voltage at 220 kV, where the regional grid is below 220 kV and is subject to line concession. A line concession is when the distribution operator has the permission for electricity distribution of a specific line up to a certain voltage. The local grid is usually below 40 kV and is subject for area concession. An area concession is when an operator has
permission for the distribution of electricity in a defined geographical area up to a certain voltage. (Ei, 2019b) Voltage levels is not the only difference, there are also variation in terms of grid topology and protection systems. The whole electrical grid is connected to one single system including all the electricity producers and consumers. Electrical equipment is constructed to operate at a frequency of 50 Hz, where one of the highest priorities for the electrical grid is to always operate at this frequency. The system needs to be balanced even if the different parts of the system affect each other. In cases when there are interruptions or failures in one part of the system, other parts need to be robust and provide the same amount of power from the other remanding working parts. If another electricity generation source cannot supply the necessary power, there is risk of system collapse. (Lindholm, 2018) The Swedish national grid is called the spine of the electrical system and is based on alternating current with high voltage lines transferring electricity long distances from the main generation plants in the north of Sweden to the south of Sweden. (Lindholm, 2018). Here, 75 % of the transmission lines consists of 400 kilovolt (kV) and 25 % are 220 kV, with a total of approximately 15 000 kilometers of transmission lines. High voltage lines are used because they are more efficient, environmentally friendly and keep transmission losses at a low level. The latter is important when transferring high amounts of electricity. Svenska kraftnät (SvK) is an authority which from the task of the Swedish government have the responsibility to manage, operate and develop a cost-effective, reliable, and environmentally adapted, national electricity grid. (SvK, 2019)
From the national grid, the electricity grid then branches into the regional grid (Lindholm, 2018). The regional grid transmits electricity with distribution lines from the national grid to the local grid and in some cases directly to consumers, such as industries. For this grid, a voltage level between 40 to 130 kV is used, with some local exceptions using lower voltages mainly consisting of overhead lines. (SvK, 2019) The reason why there is lower voltage used
in these lines is that the electricity is transported shorter distances (E. ON, 2019). Operators of the regional grids are granted a concession by Ei that gives them permission to distribute electricity on a specific line and up to a certain voltage, which in practice means that they can manage and maintain that grid. (Ei, 2019b)
From the regional grid, the electricity then branches into the local grid (Lindholm, 2018) and consists of high voltage and low voltage grids where the categorization is done by the voltage levels in the specific line (Energiföretagen, 2017). The consumers could be businesses, household, and public buildings (SvK, 2014). The electricity is transmitted to different distribution areas via lines with a high voltage between 10 to 20 kV. This voltage varies depending on the area, and (due to expansion of wind power parks) the voltage could
sometimes range to 30 kV or even higher. From the substations, the electricity is transformed to 230/400 V and transmitted in the low power lines to the majority of all consumers.
Underground cables are used in high populated areas and outside these areas overhead lines are used. (SvK, 2014) The local grid compared to the regional grid have a larger portion of smaller companies that owns the grid and have local connections to the area (E. On, 2019). Figure 2 (below) presents the Swedish electricity system schematically. The figure gives an overview of the different actors in the system with the three voltage levels.
Figure 2 A schematic overview of the Swedish electricity system with different actors in the system and with three voltage levels
3.1.1. Regulation of the Swedish electricity grid
In 1996 the Swedish electric market was de-regulated and later on, in 1998, a new authority called the Swedish Energy Agency proposed a new regulation model based on self-regulation. (Wallnerström, 2010) In 2012 a pre-regulation of electricity network tariffs was introduced; a framework is set for the electricity network tariffs and how much the grid companies can charge the customers during a four-year period. With this new system the Swedish Energy market inspectorate determines the revenue framework for each power grid company. The
revenue cap is determined by the operational cost of the electricity grid companies. In addition, the companies should be allowed a reasonable return on capital used in the business and the delivery of quality of the network services. Within the regulation, the electrical grid companies submit a revenue framework which is then approved by Ei. (Ei, 2011)
According to the Swedish electricity act, chapter 5, section 6, “the revenue framework should cover reasonable costs to operate networking during the period and provide a reasonable return on the capital required to operate operations (capital base)”. Electrical companies operate as a monopoly in one or more specific areas which they hold a concession for. (Ei, 2011)
To determine the electric grid companies’ proposals for the framework, Ei is using a standard method calculation which is supplemented by an overall assessment of every company. Here the grid companies’ revenues should cover costs and provide a reasonable return on the capital used in the business. During the assessment of the revenue framework, conditions are considered with aspects that companies cannot have an influence on, such as geographical and governmental fees. (Ei, 2011)
With the regulatory model presented in Figure 2, companies’ costs are divided into two categories; operating expenditure (OPEX) and capital expenditure (CAPEX). OPEX is then divided into two parts; non-controllable and controllable costs where the non-controllable costs for the companies could be e.g. governmental fees or costs to overlying and adjacent grids. Costs which are based on maintenance and operation are categorized as controllable costs and have efficiency requirements. This means that the companies must lower the costs between 1 to 1,82 % per year. (Næss-Schmidt et al., 2017)
In CAPEX transmission/distribution lines, substations and different sorts of facilities are included. CAPEX costs are the costs that the grid company has for using capital and can be categorized into two parts; the cost of capital wear which is the depreciation and the cost of capital binding which is the return. The revenue cap is adjusted based on two incentives, efficiency of the electrical grid (grid load and grid losses) and the continuity of supply of the grid. The indicators grid load and grid losses are used for the incentive to have an efficient utilization of the grid. While the indicators average interruption time (AIT) and the average interruption frequency (AIF) are used for the continuity of supply incentive. (Næss-Schmidt et al., 2017) The AIT and AIF are normalized which makes it easier when comparing grid companies with each other and the indicators are not affected by the yearly energy increase and decrease (Ei, 2019b).
Figure 3 (below) presents the current regulatory model, which is used by Ei in the pre-regulation of electric grid companies’ revenue cap regarding a 4-year period.
Figure 3 The regulatory model used by the Swedish energy market inspectorate in the pre-regulation process of electric grid companies’ revenue. (Inspired by Ei, 2011)
3.2.
Smart grids
Smart grids are a broad concept that includes more variable electricity production, information technologies, interactive customers, and more automation than traditional electric grids. The European Energy Regulators use the following definition for smart grids: “A Smart Grid is an electricity network that can cost efficiently integrate the behaviour and actions of all users connected to it – generators, consumers and those that do both – in order to ensure economically efficient, sustainable power system with low losses and high levels of quality and security of supply and safety.” . (CEER,2014)
The definition agrees with IEA (2011) describing a smart grid as an electricity grid that is digitized and uses advanced technology to monitor and manage the transportation of
electricity from different electricity generation sources to different types of consumers. Smart grids co-operate the capabilities of all the generation sources and grid operators with the electricity demand from the consumers to operate all parts as efficiently as possible. This means that it strives to minimize the cost and the environmental impacts while maximizing stability, resilience, and system reliability.
Electricity grids increase in importance due to trends like; growth in electric demand,
increased proportions of intermittent renewable energy sources and the need to lower carbon emissions. The smart grid technologies are supposed to meet these challenges, along with a secure clean-energy supply that is efficient and more affordable. (IEA, 2011)
The realization of the smart grid differs depending on the areas’/regions’/countries unique technical and regulatory environment. However, the main characteristics of smart grids is the following (IEA, 2011):
• Interactive
• Capable of renewable energy systems (RES) integration • Flexible
• Efficient • Resilient
Smart grids are supposed to be flexible and be able to integrate different kinds of generation sources. Conventional power plants and intermittent renewable energy sources like wind and solar generation should be able to connect to the grid without restrictions. All generation sources with different voltage levels at any location, including micro-generation, should be able to integrate into the grid. (Bayliss & Hardy, 2012) Also, energy storage should be capable of integrating into the system (IEA, 2011).
One important part of smart grids is the demand response. It is facilitated by smart meters and advanced metering infrastructure (AMI). Real-time information on the demand and price can be provided with smart meters, this allows customers to decide when, from whom and to what price, they want to buy electricity. Which allows customers to interact with the grid operators. (Bayliss & Hardy, 2012) This will help to balance the supply and demand at the same time as ensuring reliability. The intended end goal is achieved when customers get motivated to change their purchasing patterns and behaviors. (IEA, 2011)
When applying new technologies in the electric grid, the information infrastructure will collect necessary operational data in real-time. A vast amount of data will help to make the system behavior more predictable and easier to control. The integrated control, protection and communication systems require real-time data and with those, the grid can be designed to operate more optimized during different conditions. (Bayliss & Hardy, 2012)
Smart grids are supposed to be resilient and able to react at unexpected events. The grid should isolate the faulty elements while the rest of the system continues with normal
operation. These reconfiguration and self-healing actions reduce interruptions and secure a better supply of electricity. (IEA, 2011)
Table 1 (below) presents the key differences between smart grids and traditional grids (Zheng, Lin & Gao, 2013).
Table 1 Differences between smart grids and traditional grids
Smart Grid Traditional Grid
Communication Duplex Simplex
Control Pervasive Limited
Generation Distributed Centralized
Healing Self Manual
Monitoring Self Manual
Resiliency Adaptive and isolating Failures and blackouts
Sensors Through the whole system Few in the system
Type Digital Electromechanical
The possible objectives for introducing smart grids are the following (Bayliss & Hardy, 2012): • More integration of renewable energy generation sources
• Reduce greenhouse gas (GHG) emissions
• Increase the quality and reliability in the electricity supply
• Take advantage of new technology in communication, information, and power electronics • Improve the efficiency of investment in power assets
• Improved the efficiency of transmission and distribution in the grid • Provide freedom of choice and affordable electricity for customers
Smart grids can help reduce GHG emissions in two ways. One way is that smart grids enable more power sources that generate renewable electricity to be connected. The other is that the grid itself reduces its carbon footprint. (Bayliss & Hardy, 2012)
The increase in renewable energy sources is challenging for the electric grid today. Wind power and PV power produce electricity with an intermittent characteristic. They also have a higher risk of suddenly stopping their electrical generation due to high wind speeds or shadowing. These things cause disturbances in the frequency and the voltage, the
development of smart grid technologies is needed to prevent this. (Næss-Schmidt et al., 2017) Smart grids have the indirect benefit of reducing GHG emissions in society by supporting a wider introduction of EV:s and renewable generation (IEA, 2011).
Smart grid technologies provide automated services that can reduce the physical presence of technicians and engineers visiting the site where the technologies are installed and operating. This reduces travel for the grid operators and lower the GHG emissions in that aspect.
(Bayliss & Hardy, 2012) Reductions in emissions will also occur through lower line losses, feedback on energy usage, more energy efficiency in the system and energy saving due to peak load management (IEA, 2011).
Figure 4 (below) presents a visualization of a smart grid and how every part of the system is communicating, in order to co-operate the capabilities from all power generation sources and the electric demand from the consumers, to operate all parts as efficiently as possible.
Figure 4 Visualization of a smart grid
In Figure 4 vehicle to grid (V2G) is mentioned which is a concept that is later explained (see chapter 3.2.3).
The goal with the implementation of smart grids will also depend on the particular
circumstances in the specific country. In the EU:s member states, the goal with smart grids relates to the clean energy package that will be implemented in Europe, consisting of eight legislation acts. According to Ei (2019c), this package enables an accelerated transition to a sustainable energy system with higher integration of RES and energy efficiency. The new legislations create better conditions for reaching these goals by removing uncertainties in the regulation of e.g. energy storage (see chapter 3.2.4). The EU directives mentioned earlier (see chapter 1.2) gives requirements for national authorities to promote the transition of the energy system through the implementation of smart grids.
3.2.1. Distributed power generation
There are two types of power generations; centralized and distributed. The centralized power generation (CG) consists typically of big-scale power plants commonly using coal, oil, natural gas, hydro or nuclear as fuel for electricity generation. These big-scale power plants are often located far away from the customer, which requires long transmission lines for power
transmission. Using a centralized power generation system contributes to several issues such as environmental impacts, transmission, and technical losses and nuclear waste. To
2007) DG will have a central role for the future development of smart grids and potentially advance in using sustainable energy due to environmental concerns and increasing fossil-fuel price. The DG technology is an efficient and clean alternative to a conventional centralized power generation. (Singh, Jain & Østergaard, 2009) A DG system is generating electricity from several different power sources. The characteristics of a DG system is that the electricity power sources are of smaller scale compared to the CG system. (ABB, 2020) Due to this, DG has the potential to lower the transmission losses, increase the reliability and voltage stability in the system due to closer electricity production to the user (Virginia tech, 2007; Singh, Jain & Østergaard, 2009). According to Singh, Jain & Østergaard (2009), several concerns will affect how efficient and reliable the DG is. Aspects such as economics, land space, the available energy input, power quality (e.g. wind power during start and stop) and environmental concern are some factors that have an essential role in DG integration. Figure 5 (below) presents the comparison between a centralized and distributed power generation where the distributed power generation have more variations of energy sources.
Figure 5 Centralized and distributed power generation are presented where the distributed power generation contains more variations of energy sources
The produced power in a DG are often from PV, Wind, or fuel cells. The power production in a DG can have a range from e.g. 1 kW PV to 1000 MW offshore wind power. (Singh, Jain & Østergaard, 2009)
Below, technologies that can be used or have the potential to be integrated into a DG system are listed. These energy sources provide different amounts of energy for the grid (Singh, Jain & Østergaard, 2009).
• Biomass gasification • Combined cycle gas turbine • Combustion turbine • Energy storage • Fuel cells
• Hydropower
• Internal combustion engines • Microturbines
• Ocean power • Photovoltaic • Wind power
Technologies such as Gasification, Combined Cycle, Combustion turbine, microturbines, and internal combustion engines can be categorized as renewable energy sources if they use biofuels for operation (Singh, Jain & Østergaard, 2014).
3.2.2. Technologies in smart grids
There is a lot of technologies that make up a smart grid. Combined, these technologies cover the whole grid from generation, via transmission and distribution, to different electricity consumers. (IEA, 2011)
Wide-area monitoring and control (WAMC), is a real-time monitoring technology that has a display of the system components performance, over large geographical areas. This
technology monitors generation, transmission, and distribution in the grid. It is used to optimize components and learn behavior. The technology helps to mitigate wide-area disturbance and improve the transmission capacity and reliability. Information and communication technologies can be used in every part of the electric grid and enable more efficient use and management of the electric grid. (IEA, 2011)
To make a grid resilient and meet requirements, standards for advanced sensors, information exchange, reliability, and telecommunications are used. For advanced sensors, Wide-Area Measurement Systems (WAMS) is used to get a system overview and realistic operation pictures over the electrical system with advanced-technology infrastructure. Using this technology, problems and disturbances can be tracked faster and the situational awareness increases. With the information exchange, system operators should exchange suitable status information between relevant market operators such as Transmission system operators (TSO), Distribution system operators (DSO) and the customers. (Dupont et al., 2010) Technologies for transmission enhancement, includes flexible AC transmission systems (FACTS) and high voltage direct current (HVDC). FACTS are used to improve the control of transmission and maximize power transfer capability. With FACTS, existing transmission lines will get improved efficiency. HVDC lines can connect remote areas to large power areas with decreased line losses and enhanced controllability, allowing efficient use of remote energy sources. This technology also enables more renewable sources in the system, such as offshore wind farms and rural solar parks. (IEA, 2011)
AMI technologies are used between distribution and different consumers in the electric grid. AMI includes smart meters that are intended to provide a two-way flow of communication between consumers and utilities. This is useful in the following aspects. (Bayliss & Hardy, 2012; IEA, 2011; Næss-Schmidt et al., 2017)
• Time-of-use tariff schemes, where consumers are encouraged to shift their consumption to off-peak periods. For consumers, this leads to cheaper electricity.
• Real-time pricing, hourly price changes can be built into the tariff structure which follows the changes in supply and demand. This provides incentives for consumers to shift their consumption in real-time.
• Home-area networking, allows more devices to work together in a home energy
management system. A part of this is the development of more devices with sensors and internet connections. This is often referred to as “Internet of Things”, which creates more possibilities for control and automation for smart homes.
3.2.3. Smart meters
One significant component in a smart grid is the smart meter which is more advanced than a traditional electrical meter. The smart meter measures and collects certain information from the users, such as energy consumption, electrical data, frequency and voltage in real-time. With this data, the system operator can monitor the consumers and provide better reliability, monitoring, billing and optimize the power consumption. The technology is built in such a way that it communicates in a two-way connection between the smart meter and the operation system central. The smart meter remotely disconnects/connects specific loads, monitors the user and the connected devices for best demand manage in future smart buildings. (Zheng, Lin & Gao, 2013)
The smart meters provide several benefits for the customer. For example, the customer has the possibility to estimate bills with the smart meter data and with this administrate the electricity consumption and in future reduce it, which inherently have the potential to save money. The benefits for the energy operators are that the smart meter data can be used to regulate the energy pricing with the aim to limit the electricity consumption which urges the customers to use less electricity during peak periods. Below, technical characteristics a smart meter should contain are listed. (Zheng, Lin & Gao, 2013)
• Two-way communication • Data recording • Load control • Data collection • Data storing • Security • Billing • Display
Figure 6 (below) presents comparison between a typical traditional energy meter and a smart meter (Zheng, Lin & Gao, 2013).
Figure 6 Traditional electricity meter compared with a smart meter, presenting how different functionalities utilizing the two way-communication.
Ei (2017a) describes how the smart meter is important for the development of smart grids. The report mentions seven functionalities that the electric meters should have in order to promote efficient operation, increased integration of RES and interactive customers. The first requirement describes how a smart meter should be able for every phase measure voltage, current, active and reactive energy for input and output of electricity. This would allow for more energy efficiency and integration of distributed power generation. The second
requirement is that the smart meter should have an integrated display,where the customer can see their data in near real-time. Furthermore, Ei (2017a) writes that this would allow for more customer interactivity and energy efficiency. The third requirement is that the meters should be able to measure and store data about interruptions in the grid, remotely. The fourth requirement describes that the smart meter should be able to collect data about electricity consumption hourly, and for every 15 minutes. The fifth requirement is that the smart meter should be able to register interruptions longer that 3 minutes. The sixth requirement describes how the grid operators should have the opportunity to update and change the settings on the smart meters remotely. The last requirement is that it should be possible for the grid operators to connect/disconnect loads with a smart meter remotely. Ei (2017a) mention that these requirements should include all customers in the distributed grid, due to that every customer could benefit from this kind of smart meter technology.
Customers in the transmission grid do not need to have electric meters with these
requirements, because they are often high electricity consumers and already have energy expertise.
3.2.4. Energy storage
EU directive 2019/944, in article 2 (60) an energy storage facility is defined as; “a facility in the electric grid where energy is stored”. Further, in Article 2 (59), “energy storage in the electric grid is defined as a delay of the final use of the electricity to a later time than the time of production, or the conversion of electrical energy into a form of energy that can be stored, the storage of that energy, and the subsequent conversion of that energy into electrical energy or use as another energy carrier”.
With the expected increase of variable energy sources associated with a modernized grid, energy storage becomes of more importance in a smart grid. Energy storage make energy systems more flexible due to supply and demand being more balanced. The grid becomes more efficient because less produced electricity is wasted, it also affects the grid positively contributing to a higher reliability. (IRENA, 2017) Batteries could be useful for a range of ancillary services and can respond quickly, in cases like power smoothing and frequency regulation. Batteries can stabilize the grid over longer periods if it operates as a reserve or is used for peak load shaving. (IRENA, 2015)
Vattenfall (2019) claims in the article “Batteries, an important part of fossil-free energy system” that Sweden have the goal that the whole electricity production will come from RES by 2040. To manage this, complex requirements are needed to keep a stable load. One option to solve this is to use energy storage facilities.
Ei (2017b) mention that flexible resources are becoming more important in the future due to a higher proportion of variable electricity production e.g. solar and wind power. They say that flexibility is beneficial for several reasons. If the electricity production is moved to match the demand, the risk of power deficit is reduced, which reduces the need for investment in power plants and electricity grids for backup during peak loads. Batteries can also reduce the use of power generation that results in high GHG emissions. Overall, increased flexibility results in efficient use of energy resources and help to reach climate goals. (Ei, 2017b)
In the clean energy package, there are legislations for energy storage implementations. According to article 63 in the EU directive 2019/944, grid operators cannot own and operate energy storage, as long as it is not a fully integrated grid component or if the market fails to offer that service. The EU directive 2019/944 (63) “Where energy storage facilities are fully integrated network components that are not used for balancing or for congestion
management, they should not, subject to approval by the regulatory authority, be required to comply with the same strict limitations for system operators to own, develop, manage or operate those facilities. Such fully integrated network components can include energy storage facilities such as capacitors or flywheels which provide important services for network security and reliability, and contribute to the synchronization of different parts of the system.” Ei should monitor the market for energy storage at least every five years, to evaluate if there is interest from other actors to invest in energy storage. If a third party is interested in an energy storage facility, the grid operator should phase out its energy storage within 18 months. (Ei, 2020)
In the case of EV:s, problems could occur due to uncoordinated charging of EV:s that temporarily increases the demand for electricity. Energy storage systems could help balance the electricity generation during these periods. In addition to this, the EV:s have the potential to serve the electricity grid as an energy storage system. Most vehicles are parked almost 95 % of their time. During this time, EV:s could be connected to the grid and be ready to deliver or store energy through their batteries under the concept of vehicle to grid (V2G). EV:s are then used as an energy buffer for the grid. (Mwasilu et al., 2014)
According to IEA (2011) the charging pattern of EV:s is similar to the daily demand of residential consumers. This could have a negative impact on the already high peak demands during certain critical hours. However, the V2G concept could potentially minimize this impact, which is important for the deployment of electric vehicles. EV:s and charging stations that allows V2G is not commercialized. An inverter for the current is needed, in order to transfer the electricity in both directions. Development in this area is underway and the possibility to enable more flexibility and to even out the load is beneficial for the electric grid. The EV owners can also benefit from this as it provides the opportunity to buy cheaper electricity. (Sahlén, Schumacher & Lindén, 2019)
3.2.5. Demand response
Demand for electricity varies throughout the day and through the year. The electric grid is designed to meet the maximum peak demand, which means that most of the time the electric system is underutilized. Building a system to satisfy occasional peaks is not the most efficient economically approach. A smart grid can reduce the power peaks in the system with demand response. (IEA, 2011)
The goal with demand response is to optimize the power flow throughout the grid and use the assets in electricity generation, transmission, and distribution more efficiently. For the consumer, this means that the electricity consumption can be changed by responding to variable tariffs. Demand response enables loads to react in response to the supply availability. (Bayliss & Hardy, 2012) This strategy can reduce peaks in demand, provide flexibility and enable more integration of variable generation technologies (IEA, 2011). Dependent on the customer there are different strategies for demand response. These strategies are; moved electricity consumption, lowered electricity consumption, and increased electricity consumption. When a customer moves their consumption, it is often performed with heating control, EV charging or household appliances. These are activities that are required but can be done at another time. Customers that lowers their consumption are often high electricity consumers, where they lower their electricity usage during times with high electricity prices. Customers that increase their consumption are often customers with different heating options. This means that they can use electric heating during low electricity demand and switch to e.g. biofuel during high electric demand. (Ei, 2016) In Sweden, there is a technical potential in demand response both for industries and households. For Swedish industries, the demand response means that in case of high
use their own electricity production. For regular households with electrical heating, the demand response flexibility is dependent mainly on the outside temperature, where the highest demand occurs when the temperature is the lowest and the heating demand is the highest. Here the demand response can be controlled by using automatized heating control which can be controlled with information about the electricity price. In Sweden, the demand response will facilitate the integration of renewable energy systems because the customer can change their consumption periods during high electric production from RES, which will also adjust the price peaks. (Ei, 2016)
In the future, there will be several challenges for Sweden when it comes to demand response. One of the challenges is to integrate more wind power into the power system, where
frequency control, power shortage, ineffective use, and local congestion will be some off the problems to overcome. Wind power is a fluctuating power source and to compensate, hydropower is a sufficient source to minimize the fluctuations. However, the problem is, when there are too much-fluctuating energy sources integrated, hydropower will not be enough as a balancing source. Therefore, other fast demand response sources are needed. (Stensson & Piette, 2017)
3.3.
Performance indicators
The need for electricity grids to become modernized is growing, alongside this, a way to follow the development is needed. When following the development of a smart grid, a method to measure this smartness is essential. The development of smart grids can be evaluated by formulating performance indicators which can be good for several reasons. Indicators can help policymakers and authorities to elaborate incentives to improve the smart grid. The smartness of one grid can be compared to another grid in other areas or countries. Also, the results of smart grid projects that apply to development in smart grids can be evaluated. (Dupont, Meeus & Belmans, 2010)
For the performance indicators to properly asses the development of the smart grid, criteria need to be fulfilled, listed below (Dupont et al., 2010; CEER, 2014):
• Relevant, an improvement of the indicator should give a benefit to the grid users and society.
• Measurable, it should be possible to measure/calculate the value of the index in an objective way.
• Attainable, the value of the indicator can be influenced by the grid operator.
• Technology-neutral, as far as possible the indicator should remain technology-neutral
In CIRED (2020), additional criteria for indicators is listed, referring to the regulatory context in Sweden:
• Publishable, is it possible to publish the indicator?
• Ability to quantify consequences, does the indicator have the ability to quantify consequences?
• Availability, is it practically possible to collect data for the indicator from the grid operators?
• Representativeness, is the indicator representative for the smart grid development of the grid operator? Area? Or line?
3.3.1. Hosting capacity
Hosting capacity is used to calculate the electricity production that can be connected while ensuring the voltage quality and the reliability in the grid. This is relevant to the smart grid development because of increased distributed energy resources in society. The indicator can be measured as the ratio between electricity production in a distributed network to the capacity of the distribution network. The indicator is defined so the electric production in a grid do not endanger the voltage quality and the reliability. Hence, it is important to declare these limits correctly. (CEER, 2014)
High penetration of distributed energy production impacts the grid with changing current flows and more complicated voltage control. The indicator could be defined with these types of variables to limit these impacts. (CIGRE, 2014)
Bollen & Rönnberg (2017) write about several phenomena that could be included when analyzing the hosting capacity. An important factor is to decide a limit for the indicator which acts as a border between acceptable- and unacceptable performance. The different types of phenomena that can be a part of the hosting capacity are overcurrent, voltage unbalance, harmonics, over- and undervoltage.
Figure 7 (below) presents the illustration of the hosting capacity when the indicator changes with an increasing amount of generation.
Figure 7 Hosting capacity approach where the performance indicator deteriorates (Inspired by Bollen & Rönnberg, 2017)
The hosting capacity indicator should not force grid operators to do unnecessary investments in the grid, it should promote cost-effective technology. In two European countries, Italy and Norway, this indicator is used as a revenue driver, meanwhile, countries such as Austria,
Belgium, Finland, Denmark, Portugal, Slovenia, Spain, and Netherlands are either using it or considering using the indicator for monitoring. (CEER, 2014)
In 2012 in Stockholm, Sweden, a real test system was investigated for a city, rural and suburban grid where the considerate variables for the performance index were overvoltage and thermal loading. The project concluded that the overvoltage was the essential limit for the hosting capacity. By controlling the reactive power and adjust the transformer tap changers, the overvoltage issues can be limited. Further on, with the adjustment that increases the hosting capacity, the studied grid could integrate more PV in the system. (Ismael, Abdel Aleem, Abdelaziz, & Zobaa, 2018)
3.3.2. Maximum power injection
Maximum power injection is an indicator related to the hosting capacity indicator, but instead of measuring the distribution grid it refers to the transmission grid. The indicator measures the allowable maximum injection of power without congestion risks in
transmission grids. “Without congestion risks” means that the injection of power should obey the prescribed rules on operational security. The indicator can be calculated with different time ranges that are dependent on the available components and power flows. This means that the indicator value is time dependent. The value gives the largest size of production source that can be connected to the transmission grid. (CEER, 2014)
In the European countries, this indicator is not used anywhere as an earning reference. However, there are some countries that are considering or using this indicator for
monitoring, these countries are; Belgium, Germany, Italy, Lithuania, Netherlands, Portugal, Slovenia and Spain. (CEER, 2014)
3.3.3. Losses in transmission and distribution
When electricity is transported through transmission and distribution grids there will always be losses. Due to these losses, the produced energy level needs to be higher than the
consumed energy level. If at least one proportion of the electricity production is based on fossil fuels, the losses are associated with carbon-dioxide emissions. The indicator of losses in transmission and distribution could therefore be related to one of the goals with smart grid implementation that relates to minimizing GHG emissions from grid operation. (CEER, 2014)
One characteristic of smart grids is efficiency. By integrating AMI and communication technologies, grid operation could generate less losses. Therefore, losses could be useful in evaluating the efficiency of a grid and the emissions from grid operation (in the cases where electricity production included fossil emissions). (Dupont et al., 2010) The losses indicator is calculated as a ratio of the energy losses and the generated energy into the electric grid (Ei, 2019d).
𝐿𝑜𝑠𝑠𝑒𝑠 =𝐸𝑖𝑛−𝐸𝑜𝑢𝑡
𝐸𝑖𝑛 [
𝑀𝑊ℎ
𝑀𝑊ℎ] Equation 1 Losses in transmission and distribution
Where, Ein (MWh) is the generated energy into the grid and Eout (MWh) is the consumed
energy.
The losses in transmission and distribution can mainly be divided into two categories; technical losses and non-technical losses. The technical losses could be current dependent losses in the grid (e.g. heat losses), load losses and corona losses that occur at high voltage levels. The non-technical losses could be energy that is used to ensure an optimal function (e.g. transformer cooling) and electricity outage where an electricity meter is missing. (Ei, 2015)
The indicator is used for monitoring and as a revenue driver in the following countries; Austria, Great Britain, Italy, Lithuania, Norway, Poland, Portugal, Slovenia, Spain, Belgium, Germany, France, Netherlands, and Sweden (CEER, 2014; Ei, 2015).
3.3.4. Voltage variations
CIRED (2018) mention that power quality is an important aspect of the power system that cannot be neglected. To ensure an acceptable power quality, compatibility between consumer equipment and the grid is needed. The main influencers of power quality are; generation equipment, end-user equipment and the grid itself, all of which undergo changes in the development of smart grids. CIRED (2018) mentions four power quality issues that are associated with the smart grid:
• Solar panels connected to the low-voltage grids which results in overvoltage.
• Switching frequency of the converters in wind turbines causing high-frequency signals into the grid.
• Harmonics that are generated by EV charging.
• Repeated starting of heat pumps that result in light flicker
As mention by CIRED, connecting a large portion of variable renewable energy sources into the electric grid will most likely impact the power quality, causing voltage variations. A smart grid should be able to integrate a high portion of renewable sources and still ensure high power-quality to the consumers. Therefore, an indicator that relates to voltage variation is beneficial for a smart grid evaluation (Dupont et al., 2010).
Energiforsk (2015) suggest that voltage variations should be monitored, due to voltage variation being regulated in Sweden. The voltage cannot vary more than 10 % over/under the nominal value according to minimum requirements defined by Ei (EIFS 2013:1). Energiforsk (2015) proposes that a suitable indicator could measure the number of times the voltage is 5 % over or under the nominal voltage in the grid. This is not only beneficial for evaluating the quality of the electricity supply but also because variation in current can damage the
