The transition towards sustainable energy systems demands innovative solutions to overcome the challenges of integrating diverse energy carriers, fluctuating market dynamics, and operator decision-making complexities. The active involvement of local multi-carrier energy systems (LMCES) as virtual power plants in upstream energy markets is particularly hindered by the limitations of conventional optimization methods, which fail to capture the nuanced behavioral aspects of decision-making. This paper presents a novel prescriptive behavioral analytics framework for LMCES self-scheduling, integrating insights from prospect theory to address the operator’s behavioral tendencies, including loss aversion, subjective risk attitudes, and mental reference points. By embedding these behavioral considerations into a mixed integer linear programming (MILP) model, the proposed approach accounts for real-world decision-making complexities often overlooked in conventional economic theories based on rationality. Comparative analyses demonstrate that the proposed framework not only enhances the modeling of LMCES operators’ decision-making processes but also improves energy scheduling efficiency and supports sustainable energy transitions. The findings provide actionable insights for optimizing LMCES operations, advancing their role in achieving energy sustainability goals.
{"title":"Behavioral analytics for optimized self-scheduling in sustainable local multi-carrier energy systems: A prospect theory approach","authors":"Sobhan Dorahaki , S.M. Muyeen , Nima Amjady , Syed Shuibul Qarnain , Mohamed Benbouzid","doi":"10.1016/j.segan.2025.101679","DOIUrl":"10.1016/j.segan.2025.101679","url":null,"abstract":"<div><div>The transition towards sustainable energy systems demands innovative solutions to overcome the challenges of integrating diverse energy carriers, fluctuating market dynamics, and operator decision-making complexities. The active involvement of local multi-carrier energy systems (LMCES) as virtual power plants in upstream energy markets is particularly hindered by the limitations of conventional optimization methods, which fail to capture the nuanced behavioral aspects of decision-making. This paper presents a novel prescriptive behavioral analytics framework for LMCES self-scheduling, integrating insights from prospect theory to address the operator’s behavioral tendencies, including loss aversion, subjective risk attitudes, and mental reference points. By embedding these behavioral considerations into a mixed integer linear programming (MILP) model, the proposed approach accounts for real-world decision-making complexities often overlooked in conventional economic theories based on rationality. Comparative analyses demonstrate that the proposed framework not only enhances the modeling of LMCES operators’ decision-making processes but also improves energy scheduling efficiency and supports sustainable energy transitions. The findings provide actionable insights for optimizing LMCES operations, advancing their role in achieving energy sustainability goals.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101679"},"PeriodicalIF":4.8,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-13DOI: 10.1016/j.segan.2025.101677
Ángel Paredes , Jean-François Toubeau , José A. Aguado , François Vallée
Battery Energy Storage Systems (BESSs) are particularly well-suited to deepen the decarbonisation of reserve markets, traditionally dominated by non-renewable generators. BESSs operators often rely on Predict-Then-Optimise (PTO) methods to participate in these markets, which focus on forecasting market conditions without directly considering the impact of subsequent decisions during training. Recently, learning models have evolved to incorporate decision outcomes during training, known as Decision Focused Learning (DFL) methodologies, which have the potential to increase market benefits. This paper introduces a DFL approach that integrates the decision-making process of BESSs when participating in reserve markets into the training of their predictive models. By expressing the optimisation problem as a primal–dual mapping using the Karush–Kuhn–Tucker (KKT) conditions, the proposed DFL method enables the regressor to learn from the BESS’s decisions, refining its predictions based on observed outcomes, improving decision accuracy and market performance. Results show that the proposed DFL approach outperforms traditional PTO methods, with up to a 9.5% increase in profits for a case study based on the Belgian secondary reserve market, highlighting its effectiveness in managing the complexities of dynamic market conditions.
{"title":"On the participation of energy storage systems in reserve markets using Decision Focused Learning","authors":"Ángel Paredes , Jean-François Toubeau , José A. Aguado , François Vallée","doi":"10.1016/j.segan.2025.101677","DOIUrl":"10.1016/j.segan.2025.101677","url":null,"abstract":"<div><div>Battery Energy Storage Systems (BESSs) are particularly well-suited to deepen the decarbonisation of reserve markets, traditionally dominated by non-renewable generators. BESSs operators often rely on Predict-Then-Optimise (PTO) methods to participate in these markets, which focus on forecasting market conditions without directly considering the impact of subsequent decisions during training. Recently, learning models have evolved to incorporate decision outcomes during training, known as Decision Focused Learning (DFL) methodologies, which have the potential to increase market benefits. This paper introduces a DFL approach that integrates the decision-making process of BESSs when participating in reserve markets into the training of their predictive models. By expressing the optimisation problem as a primal–dual mapping using the Karush–Kuhn–Tucker (KKT) conditions, the proposed DFL method enables the regressor to learn from the BESS’s decisions, refining its predictions based on observed outcomes, improving decision accuracy and market performance. Results show that the proposed DFL approach outperforms traditional PTO methods, with up to a 9.5% increase in profits for a case study based on the Belgian secondary reserve market, highlighting its effectiveness in managing the complexities of dynamic market conditions.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101677"},"PeriodicalIF":4.8,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-10DOI: 10.1016/j.segan.2025.101672
Burak Dindar , Can Berk Saner , Hüseyin K. Çakmak , Veit Hagenmeyer
As power systems continue to evolve, the demand for effective collaboration among institutions has grown, driven by the challenges of balancing production and consumption, as well as by the increasing need for redispatch. Despite this, achieving interoperability in such a complex landscape is often hindered by concerns regarding data privacy. In response to these challenges, our paper presents a novel approach: a multi-agent system (MAS)-based AC optimal power flow (AC-OPF), empowered by machine learning (ML), designed for safeguarding data privacy and promoting interoperability. In the proposed method, the technical operator agent creates an effective dataset using n-ball, multivariate Gaussian distribution (MGD), and perturbation techniques. It also formulates valid inequalities to reduce the search space. Then, neural network (NN) models are developed to map the feasible space of the AC-OPF by utilizing only active power. Notably, these models conceal both the grid topology and sensitive data before transmission to another agent. Subsequently, the market operator agent integrates these NN models and valid inequalities into a mixed-integer linear programming (MILP) problem. This resulting MILP can be solved with various market based objective functions and constraints considering the power system limits. Thus, if there are private market-based data, they are kept confidential without being shared with the other agent. In addition, mapping is performed using the effective dataset generation technique that ensures a balanced representation of feasible and infeasible data points around the boundary. Furthermore, this effective dataset contributes to achieving remarkable accuracy in NN models, even with a relatively small volume of data points. The results on 30-, 57-, and 162-bus benchmark models of PGLib-OPF demonstrate that the proposed method can be effectively conducted while concurrently enhancing data privacy, and thus interoperability among institutions.
{"title":"Machine learning-driven multi-agent-based AC optimal power flow with effective dataset creation for data privacy and interoperability","authors":"Burak Dindar , Can Berk Saner , Hüseyin K. Çakmak , Veit Hagenmeyer","doi":"10.1016/j.segan.2025.101672","DOIUrl":"10.1016/j.segan.2025.101672","url":null,"abstract":"<div><div>As power systems continue to evolve, the demand for effective collaboration among institutions has grown, driven by the challenges of balancing production and consumption, as well as by the increasing need for redispatch. Despite this, achieving interoperability in such a complex landscape is often hindered by concerns regarding data privacy. In response to these challenges, our paper presents a novel approach: a multi-agent system (MAS)-based AC optimal power flow (AC-OPF), empowered by machine learning (ML), designed for safeguarding data privacy and promoting interoperability. In the proposed method, the technical operator agent creates an effective dataset using n-ball, multivariate Gaussian distribution (MGD), and perturbation techniques. It also formulates valid inequalities to reduce the search space. Then, neural network (NN) models are developed to map the feasible space of the AC-OPF by utilizing only active power. Notably, these models conceal both the grid topology and sensitive data before transmission to another agent. Subsequently, the market operator agent integrates these NN models and valid inequalities into a mixed-integer linear programming (MILP) problem. This resulting MILP can be solved with various market based objective functions and constraints considering the power system limits. Thus, if there are private market-based data, they are kept confidential without being shared with the other agent. In addition, mapping is performed using the effective dataset generation technique that ensures a balanced representation of feasible and infeasible data points around the boundary. Furthermore, this effective dataset contributes to achieving remarkable accuracy in NN models, even with a relatively small volume of data points. The results on 30-, 57-, and 162-bus benchmark models of PGLib-OPF demonstrate that the proposed method can be effectively conducted while concurrently enhancing data privacy, and thus interoperability among institutions.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101672"},"PeriodicalIF":4.8,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The synergistic integration of the smart grid and smart transportation network presents a wealth of data pertaining to the main grid and transportation infrastructure, offering valuable insights for electric vehicle (EV) owners to navigate their vehicles efficiently. However, the unpredictable nature of traffic conditions, charging prices, and waiting times at charging stations poses a significant challenge to achieving optimal EV navigation. In response to this challenge, a novel navigation system is proposed that strives to minimize both total travel time and charging costs at charging stations. The approach of this paper involves leveraging a unique methodology to determine the shortest path to the optimal charging station, which will be one of the renewable charging station, non-renewable charging station and mobile EV chargers, employing Dijkstra's algorithm for efficient route planning. The system takes into account real-time data on traffic dynamics, charging station availability, and pricing fluctuations to dynamically adjust navigation routes, ensuring that EV owners can make informed decisions on the go. To validate the effectiveness of the proposed approach, a series of experiments are conducted. The results demonstrate the system's ability to optimize both travel time and charging costs, providing a practical solution for EV navigation in the face of unpredictable variables. These findings validate the effectiveness of the proposed system in optimizing EV navigation under dynamic and uncertain conditions, offering practical solutions for diverse EV mobility configurations.
{"title":"Optimal electric vehicle navigation through smart grid synergy and innovative routing strategies","authors":"Sima Maleki, Mahdiyeh Eslami, Mahdi Jafari Shahbazzadeh, Alimorad Khajehzadeh","doi":"10.1016/j.segan.2025.101669","DOIUrl":"10.1016/j.segan.2025.101669","url":null,"abstract":"<div><div>The synergistic integration of the smart grid and smart transportation network presents a wealth of data pertaining to the main grid and transportation infrastructure, offering valuable insights for electric vehicle (EV) owners to navigate their vehicles efficiently. However, the unpredictable nature of traffic conditions, charging prices, and waiting times at charging stations poses a significant challenge to achieving optimal EV navigation. In response to this challenge, a novel navigation system is proposed that strives to minimize both total travel time and charging costs at charging stations. The approach of this paper involves leveraging a unique methodology to determine the shortest path to the optimal charging station, which will be one of the renewable charging station, non-renewable charging station and mobile EV chargers, employing Dijkstra's algorithm for efficient route planning. The system takes into account real-time data on traffic dynamics, charging station availability, and pricing fluctuations to dynamically adjust navigation routes, ensuring that EV owners can make informed decisions on the go. To validate the effectiveness of the proposed approach, a series of experiments are conducted. The results demonstrate the system's ability to optimize both travel time and charging costs, providing a practical solution for EV navigation in the face of unpredictable variables. These findings validate the effectiveness of the proposed system in optimizing EV navigation under dynamic and uncertain conditions, offering practical solutions for diverse EV mobility configurations.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101669"},"PeriodicalIF":4.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-06DOI: 10.1016/j.segan.2025.101671
Abhishek Tiwari , Bablesh K. Jha , Naran M. Pindoriya
This article proposes an adaptable incentive framework for an incentive-based demand response (IBDR) program. The framework is based on changes in demand from end-consumers using the bilevel approach to optimize the scheduling of flexible loads. The distribution system operator (DSO) acts as a leader with a multi-objective optimization problem. The objective is to maximize profit while minimizing network energy loss and peak load at the point of common coupling. The DSO’s strategy involves changing demand-based adaptive incentive offers to enhance end-consumers participation in the DR program. Furthermore, the DSO aimed to mitigate phase unbalancing as an objective to address power quality issues caused by imbalances in phase voltage and power. Aggregators are regarded as followers in the bilevel approach, aiming to maximize incentives for mitigating the discomfort caused by scheduling flexible energy resources in the IBDR program. By utilizing Karush-Kuhn–Tucker conditions, the previously mentioned bilevel problem transformed into a single-level optimization problem. This work examined two case studies to determine the effectiveness of the proposed adaptable IBDR model. The efficacy of the proposed framework was assessed on a modified IEEE 25 bus unbalanced distribution system. The evaluation reveals that adaptive IBDR confers advantages to all participants, including DSO and end-consumers.
{"title":"Incentive-based demand response program with phase unbalance mitigation: A bilevel approach","authors":"Abhishek Tiwari , Bablesh K. Jha , Naran M. Pindoriya","doi":"10.1016/j.segan.2025.101671","DOIUrl":"10.1016/j.segan.2025.101671","url":null,"abstract":"<div><div>This article proposes an adaptable incentive framework for an incentive-based demand response (IBDR) program. The framework is based on changes in demand from end-consumers using the bilevel approach to optimize the scheduling of flexible loads. The distribution system operator (DSO) acts as a leader with a multi-objective optimization problem. The objective is to maximize profit while minimizing network energy loss and peak load at the point of common coupling. The DSO’s strategy involves changing demand-based adaptive incentive offers to enhance end-consumers participation in the DR program. Furthermore, the DSO aimed to mitigate phase unbalancing as an objective to address power quality issues caused by imbalances in phase voltage and power. Aggregators are regarded as followers in the bilevel approach, aiming to maximize incentives for mitigating the discomfort caused by scheduling flexible energy resources in the IBDR program. By utilizing Karush-Kuhn–Tucker conditions, the previously mentioned bilevel problem transformed into a single-level optimization problem. This work examined two case studies to determine the effectiveness of the proposed adaptable IBDR model. The efficacy of the proposed framework was assessed on a modified IEEE 25 bus unbalanced distribution system. The evaluation reveals that adaptive IBDR confers advantages to all participants, including DSO and end-consumers.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101671"},"PeriodicalIF":4.8,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143593192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper deals with the simultaneous calculation of the series compensation degrees of multiple lines in a transmission grid, in order to achieve the desired active power flows in the compensated lines, using an analytical approach based on the compensation theorem. The paper extends to the case of multiple compensated lines the methodological approach already proposed by the authors for a single compensated line. The mathematical basis beneath the proposed method is first presented and later the method is applied and validated using two test networks. The first network is a 10 buses, 14 lines customization of a portion of an existing 400 kV – 50 Hz transmission network. The second network represents a 400 kV – 50 Hz large real network consisting of 78 nodes and 109 overhead lines for a total extension of 10,900 km, including 17 series-compensated lines. The results show the robustness and accuracy of the analytical method proposed, which has the advantage to be simpler than the classical approach based on power flow procedures. Accordingly, the novel method proposed proves to be an interesting alternative to the classical calculation procedures.
{"title":"Computation of multiple series compensations using the compensation theorem","authors":"Fabio Massimo Gatta , Davide Lauria , Stefano Quaia , Stefano Lauria","doi":"10.1016/j.segan.2025.101673","DOIUrl":"10.1016/j.segan.2025.101673","url":null,"abstract":"<div><div>This paper deals with the simultaneous calculation of the series compensation degrees of multiple lines in a transmission grid, in order to achieve the desired active power flows in the compensated lines, using an analytical approach based on the compensation theorem. The paper extends to the case of multiple compensated lines the methodological approach already proposed by the authors for a single compensated line. The mathematical basis beneath the proposed method is first presented and later the method is applied and validated using two test networks. The first network is a 10 buses, 14 lines customization of a portion of an existing 400 kV – 50 Hz transmission network. The second network represents a 400 kV – 50 Hz large real network consisting of 78 nodes and 109 overhead lines for a total extension of 10,900 km, including 17 series-compensated lines. The results show the robustness and accuracy of the analytical method proposed, which has the advantage to be simpler than the classical approach based on power flow procedures. Accordingly, the novel method proposed proves to be an interesting alternative to the classical calculation procedures.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101673"},"PeriodicalIF":4.8,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143593193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The paper introduces a technical-economic methodology to estimate the additional inertia required in a Transmission Network for future scenarios and presents an algorithm to optimally dispatch it among different sources and interwork busbars. First, the amount of inertia is calculated to constrain the Rate of Change of Frequency (RoCoF) within sustainable limits. Then, such inertia is allocated accounting for the contributions from Renewable Energy Sources (RESs) and Battery Energy Storage Systems (BESSs), complemented by the deployment of Synchronous Compensators (SCs) across various nodes of a Transmission Network. The methodology underwent testing within the Italian Transmission Network, utilizing the informational support furnished by the Italian Transmission System Operator (TSO). Despite its simplicity, the results exhibit notable accuracy, validated through rigorous comparisons with detailed time-domain simulations. Moreover, the low computational cost of the method, allowed a statistical analysis considering all the hours of year 2030, to get information on the distributions of the quantities of interest.
{"title":"Optimal inertia allocation in future transmission networks: A case study on the Italian grid","authors":"Manuela Minetti , Matteo Fresia , Renato Procopio , Andrea Bonfiglio , Gio Battista Denegri , Giuseppe Lisciandrello , Luca Orrù","doi":"10.1016/j.segan.2025.101676","DOIUrl":"10.1016/j.segan.2025.101676","url":null,"abstract":"<div><div>The paper introduces a technical-economic methodology to estimate the additional inertia required in a Transmission Network for future scenarios and presents an algorithm to optimally dispatch it among different sources and interwork busbars. First, the amount of inertia is calculated to constrain the Rate of Change of Frequency (RoCoF) within sustainable limits. Then, such inertia is allocated accounting for the contributions from Renewable Energy Sources (RESs) and Battery Energy Storage Systems (BESSs), complemented by the deployment of Synchronous Compensators (SCs) across various nodes of a Transmission Network. The methodology underwent testing within the Italian Transmission Network, utilizing the informational support furnished by the Italian Transmission System Operator (TSO). Despite its simplicity, the results exhibit notable accuracy, validated through rigorous comparisons with detailed time-domain simulations. Moreover, the low computational cost of the method, allowed a statistical analysis considering all the hours of year 2030, to get information on the distributions of the quantities of interest.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101676"},"PeriodicalIF":4.8,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.1016/j.segan.2025.101665
Mehran Jafari , Gautier Bureau , Marco Chiaramello , Adrien Guironnet , Patrick Panciatici , Petros Aristidou
In recent decades, the number of smart digital controllers employed in electric power systems has increased drastically. Either from the modernization of existing analog ones or the introduction of new, data-driven, or even optimization-based controllers, they are now dominating the behavior of power systems. However, this introduces a challenge in the simulation of power system dynamics, as the existing numerical simulation methods are very time-consuming when tackling the resulting hybrid differential-algebraic systems. In this paper, a novel interpolation-based method is proposed for performing fast and accurate dynamic simulations of electric power systems equipped with smart digital controllers. This method fully exploits the potential of variable time-step integration methods without requiring a time-step reduction in the case of discrete events stemming from digital controllers. Therefore, it accelerates the numerical simulation of large-scale systems containing many non-equation-based smart digital controllers, while maintaining accuracy. The performance of the proposed method is showcased using both conventional and smart digital controllers.
{"title":"Fast and accurate simulation of smart digital controllers in power system dynamic studies","authors":"Mehran Jafari , Gautier Bureau , Marco Chiaramello , Adrien Guironnet , Patrick Panciatici , Petros Aristidou","doi":"10.1016/j.segan.2025.101665","DOIUrl":"10.1016/j.segan.2025.101665","url":null,"abstract":"<div><div>In recent decades, the number of smart digital controllers employed in electric power systems has increased drastically. Either from the modernization of existing analog ones or the introduction of new, data-driven, or even optimization-based controllers, they are now dominating the behavior of power systems. However, this introduces a challenge in the simulation of power system dynamics, as the existing numerical simulation methods are very time-consuming when tackling the resulting hybrid differential-algebraic systems. In this paper, a novel interpolation-based method is proposed for performing fast and accurate dynamic simulations of electric power systems equipped with smart digital controllers. This method fully exploits the potential of variable time-step integration methods without requiring a time-step reduction in the case of discrete events stemming from digital controllers. Therefore, it accelerates the numerical simulation of large-scale systems containing many non-equation-based smart digital controllers, while maintaining accuracy. The performance of the proposed method is showcased using both conventional and smart digital controllers.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101665"},"PeriodicalIF":4.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-28DOI: 10.1016/j.segan.2025.101662
Fabian Krug , Laura Maier , Dirk Müller
To integrate volatile renewable electricity generation into the grid, electricity consumption needs to be adapted to the generation by so-called demand response. In the residential scope, demand response can be realized with heat pumps and electric vehicles. Recent research has already quantified the flexibility potential of heat pumps and electric vehicles. However, none of this research considers the communication protocols used in practical applications. The novelty of the current work is that it analyzes the influence of different real-world communication protocols on the flexibility potential of heat pumps and electric vehicles. Therefore, models for heat pumps and electric vehicles with different communication protocols are developed and verified with measurement data. To determine the flexibility potential, 200 randomized instances of these models are simulated over one year for each communication protocol. The results show that the downward flexibility potential of heat pumps is independent of the communication protocol. The upward flexibility potential of heat pumps is highest and most stable with direct load control over Modbus. Unlike downward flexibility, upward flexibility increases the daily energy consumption of heat pumps. For electric vehicles, ISO 15118-20 shows the highest overall flexibility.
{"title":"Quantifying the demand response potential of heat pumps and electric vehicles considering communication protocol constraints","authors":"Fabian Krug , Laura Maier , Dirk Müller","doi":"10.1016/j.segan.2025.101662","DOIUrl":"10.1016/j.segan.2025.101662","url":null,"abstract":"<div><div>To integrate volatile renewable electricity generation into the grid, electricity consumption needs to be adapted to the generation by so-called demand response. In the residential scope, demand response can be realized with heat pumps and electric vehicles. Recent research has already quantified the flexibility potential of heat pumps and electric vehicles. However, none of this research considers the communication protocols used in practical applications. The novelty of the current work is that it analyzes the influence of different real-world communication protocols on the flexibility potential of heat pumps and electric vehicles. Therefore, models for heat pumps and electric vehicles with different communication protocols are developed and verified with measurement data. To determine the flexibility potential, 200 randomized instances of these models are simulated over one year for each communication protocol. The results show that the downward flexibility potential of heat pumps is independent of the communication protocol. The upward flexibility potential of heat pumps is highest and most stable with direct load control over <em>Modbus</em>. Unlike downward flexibility, upward flexibility increases the daily energy consumption of heat pumps. For electric vehicles, ISO 15118-20 shows the highest overall flexibility.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101662"},"PeriodicalIF":4.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-27DOI: 10.1016/j.segan.2025.101654
Cong Zhang
With the increasing number of electric vehicles (EVs), the "vehicle road network" system will face operational uncertainties in both the road network and the power grid. This article considers the uncertainty of the operation status of "vehicle road network". And a robust optimization scheduling strategy for "vehicle road network" considering dual uncertainty is proposed. Firstly, based on the Stackelberg game theory, a "vehicle road network" system optimization scheduling architecture is proposed. Secondly, in response to the optimization problem of operating costs for upper level leaders in the distribution network (DN), a robust optimization model for DN is constructed considering the uncertainty of photovoltaic power generation to achieve optimization of operating costs for the distribution network. On the basis of considering the uncertainty of travel time caused by road network, lower level followers of EVs participate in demand response based on the charging prices which are set by the distribution network. The simulation results show that the proposed optimization scheduling strategy can effectively reduce the operating costs of DN. The traffic pressure is alleviated. And the operational efficiency of fast charging stations is improved.
{"title":"A robust optimization scheduling strategy for \"vehicle-road-network\" systems considering dual uncertainty","authors":"Cong Zhang","doi":"10.1016/j.segan.2025.101654","DOIUrl":"10.1016/j.segan.2025.101654","url":null,"abstract":"<div><div>With the increasing number of electric vehicles (EVs), the \"vehicle road network\" system will face operational uncertainties in both the road network and the power grid. This article considers the uncertainty of the operation status of \"vehicle road network\". And a robust optimization scheduling strategy for \"vehicle road network\" considering dual uncertainty is proposed. Firstly, based on the Stackelberg game theory, a \"vehicle road network\" system optimization scheduling architecture is proposed. Secondly, in response to the optimization problem of operating costs for upper level leaders in the distribution network (DN), a robust optimization model for DN is constructed considering the uncertainty of photovoltaic power generation to achieve optimization of operating costs for the distribution network. On the basis of considering the uncertainty of travel time caused by road network, lower level followers of EVs participate in demand response based on the charging prices which are set by the distribution network. The simulation results show that the proposed optimization scheduling strategy can effectively reduce the operating costs of DN. The traffic pressure is alleviated. And the operational efficiency of fast charging stations is improved.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101654"},"PeriodicalIF":4.8,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143600684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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