Search

Massive and diversified distributed energy resources (DERs) have great potential for improving the flexibility of power system operations. However, given the wide distribution, small capacity, and variable ownership subject characteristics of DERs, virtual power plants (VPPs) are suitable for aggregating the flexible DERs for participation in the wholesale energy market. Extant studies have focused on obtaining bidding strategies in multiple electricity markets. However, there are shortcomings in the relevant market mechanisms and temporal relationships for participating in energy and ancillary service markets. This work proposes a joint market temporal rolling bidding strategy for VPPs in the wholesale market based on real unified electricity market mechanisms in the southern region of China. Accordingly, the proposed method enhances the market-oriented operational profits of VPPs. [ABSTRACT FROM AUTHOR]

Non-wires alternatives and microgrid technologies are maturing and present great opportunities for electric utilities to increase the benefits they offer to their customers. They have the potential to decrease the cost of resolving traditional electrical system loading issues, contribute to carbon emissions reductions, and improve the electrical distribution system's resilience to extreme weather events. The authors of this manuscript present a review of the research on microgrids and their practical applications. This is leveraged with the past work of the authors of this manuscript and other authors to develop specific objectives for microgrids, practical criteria for engineers to consider when deploying microgrids, stochastic methods to optimize microgrid designs, and black start requirements. This guidance is then used for the design of actual networked microgrids being deployed with adaptive boundaries. [ABSTRACT FROM AUTHOR]

With the large-scale distributed energy resources (DERs) interfaced by power electronic converters connected to the power grid, the traditional synchronous generation is gradually replaced, and the inertia and primary frequency regulation capability of the power system is weakened, which seriously threatens the frequency security of the power grid. In view of the above problems, a hierarchical control strategy by coordinating the distributed wind turbine (WT) and photovoltaic (PV) is presented to improve the transient frequency (TF) and steady-state frequency (SSF). For the system control level, the optimal frequency regulation coefficients of multiple WTs and PVs clustering systems are determined by the optimal control principle with minimum frequency regulation energy, while considering the constraints of TF and SSF. For the WT/PV control level, the frequency regulation coefficients of WTs and PVs are solved by the model predictive controller (MPC) and alternating direction method of multipliers (ADMM), with the minimum control cost of WT and PV. It achieves the optimal allocation of frequency regulation power and improves the computing efficiency. Finally, the validity of the proposed method is tested in the modified IEEE 10-machine 39-bus system. [ABSTRACT FROM AUTHOR]

Abstract With the large-scale distributed energy resources (DERs) interfaced by power electronic converters connected to the power grid, the traditional synchronous generation is gradually replaced, and the inertia and primary frequency regulation capability of the power system is weakened, which seriously threatens the frequency security of the power grid. In view of the above problems, a hierarchical control strategy by coordinating the distributed wind turbine (WT) and photovoltaic (PV) is presented to improve the transient frequency (TF) and steady-state frequency (SSF). For the system control level, the optimal frequency regulation coefficients of multiple WTs and PVs clustering systems are determined by the optimal control principle with minimum frequency regulation energy, while considering the constraints of TF and SSF. For the WT/PV control level, the frequency regulation coefficients of WTs and PVs are solved by the model predictive controller (MPC) and alternating direction method of multipliers (ADMM), with the minimum control cost of WT and PV. It achieves the optimal allocation of frequency regulation power and improves the computing efficiency. Finally, the validity of the proposed method is tested in the modified IEEE 10-machine 39-bus system.

Sustainable frameworks are being introduced by technological breakthroughs to address the continuously increasing demand for energy and this article presents a novel framework for a Virtual Power Plants to sustain and optimize Distributed Energy Resources (DERs) administration through blockchain technology. The multi-layered architecture integrates blockchain technology with other VPP components, such as a cloud service provider, grid and transmission operator, and forecasting of DERs, to optimize bidding and scheduling in real-time across multiple markets to enhance energy efficiency and grid stability. By employing a system engineering methodology, this framework incorporates smart meters to facilitate the transmission and reception of data i.e., voltage, and current between participants, and DERs within the network. This strategy makes it easier to advance sustainable energy sources, which advances predictive capabilities, strengthens the framework’s resilience, and protects the environment. The investigation of demand response, functional and system trade-off analysis, configuration selection, and other subsystems within a decentralized network offers insights into the necessary components and challenges involved in the construction of such a system. This study contributes comprehensive assistance and valuable insights to both practitioners and researchers involved in the field of sustainable energy transition through Virtual Power Plant.

The electric vehicle (EV) industry has emerged in response to the necessity of reducing greenhouse gas emissions and combating climate change. However, as the number of EVs increases, EV charging networks are confronted with considerable obstacles pertaining to accessibility, charging time, and the equilibrium between electricity demand and supply. In this paper, we present a techno-economic analysis of EV charging stations (EVCSs) by building type. This analysis is based on public EVCS data and considers both standalone local grid operation and integrated operation of distributed energy resources (DERs) and the local grid. The analysis has significant implications for the management of the electricity grid and the utilization of sustainable energy, and can result in economic benefits for both residential, commercial, and public buildings. The analysis indicates that integrating DERs with the local grid at EV charging stations can reduce local grid usage relative to EV demand. Nevertheless, there are also complexities, such as initial investment and maintenance costs, especially the weather-dependent performance variability of solar, which require financial support mechanisms, such as subsidies or tax incentives. Future research should focus on different DER integrations, regional and seasonal variability, user behavior, installation location, policy and regulatory impacts, and detailed capital expenditure analysis. Such research will advance DER and EVCS integration and contribute to increasing the efficiency and sustainability of urban energy systems. [ABSTRACT FROM AUTHOR]

The publication considers the modeling of the operating modes of inverter equipment used in low-voltage Microgrids. Provided description of the developed Microgrid simulation model, one of the components of which is a bidirectional semiconductor converter with a control system based on the application of abc-dq0 transformation. Was determined the influence of the presence of inverter equipment operating in the mode of current generation and regulation of reactive power on the indicators of the quality of power supply in the Microgrid, taking into account the transient processes that occur when connecting powerful loads. References 10, figures 4. [ABSTRACT FROM AUTHOR]

As transportation evolves with greater adoption of electric vehicles (EVs), vehicle-to-vehicle (V2V) energy trading stands out as an important innovation for managing energy resources more effectively as it reduces dependency on traditional energy infrastructures and, hence, alleviates the pressure on the power grid during peak demand times. Thus, this paper conducts a systematic review of the V2V energy trading frameworks. Through the included article analysis (n = 61), this paper discusses the state-of-the-art energy trading frameworks' structure, employed methodologies, encountered challenges, and potential directions for future research. To the best of the authors' knowledge, this is the first review explicitly focused on V2V energy trading. We detail four critical challenges to face while establishing the framework in current research, providing an overview of various methodologies, including auctions, blockchain, game theory, optimisation, and demand forecasting, that are used to address these challenges and explore their integration within the research landscape. Additionally, this paper forecasts the evolution of V2V energy trading, highlighting the potential incorporation of advanced and established technologies like artificial intelligence (AI), digital twins, and smart contracts. This review aims to encapsulate the existing state of V2V energy trading research and stimulate future advancements and technological integration within the field. [ABSTRACT FROM AUTHOR]

As the adoption of distributed energy resources (DERs) grows, the future of electricity distribution systems is confronted with significant challenges. These challenges arise from the transformation of consumers into prosumers and the resulting increased system complexity, leading to more pressure on the distribution grids. To address this complexity, a Digital Twin framework is designed to simulate DERs within distribution grids effectively. This framework is structured around four key modules: DERs, the electricity distribution grid, the energy management system, and the consumers. It incorporates a communication interface to facilitate interactions among these modules and includes considerations for grid topologies and demand-side configurations. The framework allows for the exploration of various DER adoption rates and capacities. The validation of this framework involves case studies on two Danish distribution grids with scenarios incorporating rooftop photovoltaic (PV) systems, batteries, and electric vehicles, considering different combinations of these technologies. The findings demonstrate the framework's ability to depict the states of the grid, PV systems, electric vehicles, and battery systems with a 10 min resolution over periods ranging from a day to over a decade. [ABSTRACT FROM AUTHOR]

Abstract.-: The recent advancements in energy production, storage, and distribution are creating unprecedented opportunities in the field. Major consumers can benefit from the implementation of distributed energy resource networks capable of generating electricity or heating from sources, often renewable ones, in close proximity to the point of use, rather than relying on centralized generation sources from power plants. In this paper, we introduce a pioneering model designed to determine the optimal set of energy commands in a distributed energy resource network, minimizing operational costs in a time horizon. Indeed, we propose an innovative mixed-integer linear programming formulation rooted in the production-inventory models commonly employed in aggregate production planning. The system integrates diverse energy generation sources, storage facilities, and demand points, encompassing both electric and heating commodities. The optimum of the model is achieved for all analyzed instances of the test library (2 scenarios-20 instances) in an exceptionally short time, outperforming other approaches previously presented in the literature. We employed the Gurobi optimizer to solve the model, obtaining rapid responses that ensure real-time decision-making and facilitate effective control of the distributed energy resource network within a three-days’ rolling horizon, as discussed in a simulated real-life application case study. Indeed, the proposed model solves in less than 1 s, enabling near-instantaneous decision-making. This swift solution time surpasses any known references in the field, effectively shifting the bottleneck in DER network operation from the decision-making process to the forecasting of demand and weather conditions. While forecasting typically requires a minimum of 15 min, our approach suggests that a reduction in this forecasting time could further enhance the control system's response time, given the model's ability to deliver optimal solutions almost immediately. The real-time availability of optimal solutions allows for the seamless incorporation of stochastic elements into the control loop via a rolling horizon process.

Massive and diversified distributed energy resources (DERs) have great potential for improving the flexibility of power system operations. However, given the wide distribution, small capacity, and variable ownership subject characteristics of DERs, virtual power plants (VPPs) are suitable for aggregating the flexible DERs for participation in the wholesale energy market. Extant studies have focused on obtaining bidding strategies in multiple electricity markets. However, there are shortcomings in the relevant market mechanisms and temporal relationships for participating in energy and ancillary service markets. This work proposes a joint market temporal rolling bidding strategy for VPPs in the wholesale market based on real unified electricity market mechanisms in the southern region of China. Accordingly, the proposed method enhances the market-oriented operational profits of VPPs.

The increasing penetration of distributed energy resources and the growing electrification of end-use consumption complicate energy management. Current strategies, which rely on centralized systems for peer-to-peer interactions, face issues of scalability, security, traceability, single points of failure, and privacy. Blockchain, with its decentralized nature, offers immutability, transparency, automation, and scalability as potential solutions. However, practical implementation remains challenging. This study systematically reviews 156 studies published since 2021 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to explore these limitations. Results highlight that selecting the appropriate blockchain platform and consensus mechanism is crucial. Observations show that 42% of studies proposing new consensus methods and 46% developing new platforms or using simulations struggle with practical implementation. In contrast, leveraging existing standard blockchain platforms and consensus mechanisms enhances practicality and scalability. Additionally, seamless interoperability with Internet of Things (IoT) remains a significant challenge due to the high costs associated with the few platforms that offer this feature. Standardization of blockchain methodology, interoperability, performance measurement, and governance remains a major issue despite several parallel efforts by multiple stakeholders, including blockchain platform providers. Consolidation of these efforts into a common framework, together with the utilization of existing blockchain components, is key to resolving current limitations and fostering wider adoption. Among the various blockchain components suited for different applications, this study provides key criteria for selection, guiding the development of practical and scalable blockchain-based energy applications.

This paper considers a single-machine scheduling problem with sequence-dependent setup times and energy-generation and storage systems. Each job requires a sequence-dependent setup to be processed on the machine, and both setup and processing of the job require job-dependent amounts of energy. The energy consumed by the machine can be bought from an Electric Power Company (EPC) or generated by own Distributed Energy Resource (DER), such as solar photovoltaic or wind, and the energy can be stored in an Energy Storage System (ESS). The objective is to minimise the total cost, the sum of production cost depending on makespan and energy cost by considering energy usage from the EPC, DER and ESS. For the problem, a mathematical programming model is first derived by using period-based indexes. Then, a hybrid genetic algorithm, which adds appropriate idle times between jobs and determines an efficient energy schedule by storing some energy during less expensive periods into the ESS for later use in high-price periods, is developed. Finally, computational experiments show that the proposed algorithm provides effective solutions, and which component of the total cost affects the performance the most, how effective adding idle times between jobs is, and how much cost can be saved by having a DER and ESS. [ABSTRACT FROM AUTHOR]

Electricity, as a sustainable energy carrier, plays a central role in the transition scenarios for carbon neutralization of energy systems. Expanding the potential of electricity requires intelligent integration of electricity infrastructures and electricity markets with distributed energy resources (DERs) including roof‐top solar photovoltaics (PVs), controllable loads, and storage batteries. This integration will advance the management of power distribution grids, resulting in harmonization of the distribution grids and variable renewable generation. In the foreseeable future, the DERs will be actively used to perform advanced management of power distribution grids and compensate fluctuation in variable renewable power generation such as PVs and wind power. Market‐based coordination mechanisms are already practiced in large‐scale electricity production, electricity trading and transmission around the world. Using these mechanisms at the distribution grid level to integrate numerous flexible devices into the grids remains an important research topic. This paper reviews the role of DER integration toward carbon‐neutral power systems, the state‐of‐art and challenges associated with market‐based approaches for integrating flexible demand, distributed generation, and storage. Methods for assessing the value of DER aggregation such as virtual power plants and microgrids are also explored. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The constant need for fuel to meet the commercial sector's ever-increasing demand has driven researchers to discover and optimize renewable energy resources, paving the way for sustainable production of reliable and clean energy resources. The goal of the current work is to close the gap in process parameter optimization needed to convert wind energy wake from traffic on highways into electrical energy utilizing vertical-axis wind turbines (VAWTs). The energy output from the VAWT is analyzed to investigate how it is impacted by the variations in multiple parameter settings. Using the central composite design (CCD), a threelevel four-factor array was used to investigate the following parameters: VAWT vertical distance (VD) and horizontal distance (HD) as continuous parameters, while road side (S) and location (L) of VAWT as categorical parameters. To find the most important parameter, response surface methodology (RSM) optimization and an analysis of variance (ANOVA) test are performed. L accounts for 66.67% of the total variable, with S coming in second with 51.80%. Using the best results from RSM and ANOVA, a confirmation test is run, and the results show yields of 88.75% ±0.05% and 87.5% ±0.05%, respectively. Therefore, RSM and ANOVA can be utilized equally for optimization at the same VAWT design. Lastly, the findings of the economic and environmental evaluation demonstrate that, in comparison to the basic settings, VAWT operating at optimal settings can save up to 180% and 200% more energy and reduce carbon emissions, respectively. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for jointly optimizing the sizing and operation of AC microgrids. The methodology is applied to the CAMPUSGRID microgrid, which is currently being developed at the State University of Campinas (UNICAMP) in Brazil. The distributed energy resources (DERs) considered in the study include a battery energy storage system (BESS), a dispatchable gas generator, and photovoltaic (PV) generation. The proposed energy management system (EMS) minimizes the costs associated with day-ahead operation of the DERs in an unbalanced three-phase AC microgrid. To develop the EMS as a mixed-integer linear programming (MILP) model, the MATLAB Link module of HOMER Pro and the AMPL API are used. Heuristic rules are also developed for contingency and post-contingency operations in case of unintentional disconnection from the main grid. The sizing simulations combine tariff regimes, contingencies, and dispatch strategies. The results demonstrate the influence of the tariff on the net present cost (NPC) and the importance of resilience in sizing DERs. Additionally, the proposed model is capable of assessing the impact of DERs on the electrical grid. Finally, the joint strategy proposed in this paper leads to a reduction in microgrid investment cost, improvement in voltage magnitude profile, and reduction in active power losses when compared to the default dispatch strategies of HOMER Pro. [ABSTRACT FROM AUTHOR]

A virtual power plant (VPP) refers to an active aggregator of heterogeneous distributed energy resources (DERs), which creates a promising pathway to expand renewable energy and demand-side electrification for deep decarbonization. The VPP market is projected to have a significant growth potential, with the global investment surging from $6.47 billion in 2022 to $16.90 billion by 2030. Up to now, VPPs still face technical challenges in dealing with the inherent uncertainty of DERs, and data emerge as a promising and essential resource to handle this issue. This paper makes the first effort to review the development of VPP technologies from a data-centric perspective, and then analyze the major role of data within every decision phase of VPPs. We examine the VPP energy management through a data lifecycle lens, and extensively survey the progress in data creation, data communication, data-driven decision support, data sharing and privacy, as well as technical solutions stemming from reinforcement learning, peer-to-peer sharing, blockchain, and market participation. In addition, we offer a unique overview of open data and recent real-world projects around the world to showcase the latest VPP practices. We finally discuss the major challenges and future opportunities in detail, with a focus on topics such as public benchmarks, internet of things, 5G, explainable artificial intelligence, and federated learning. We highlight the need for technical advances in data management and support systems for the growing scale of future VPP systems, and suggest VPPs delivering more ancillary grid services in the future.

Conventional centralized optimization and management approaches may not work well in an emerging and distributed energy system with a high penetration of electric vehicles and green energy sources. The usage of blockchain technology is growing as a strong competitor as it can provide this kind of market with a transparent, secure, and efficient transactional platform. Nevertheless, most energy systems usually depend on complex mathematical optimization, which is poorly incorporated into blockchain applications. Moreover, time-sensitive message dissemination requirements, resource-intensiveness, high computational load, and communication overhead of the traditional blockchain consensus mechanisms make it difficult to connect with real-time vehicular networks. Here, we employ Proof of Intelligence (PoI), a novel prosumer-centric blockchain consensus mechanism to develop a comprehensive model of trust based on commitments of supply and demand through the application of peer-to-peer energy exchange with effective and dynamic integration of renewable sources and electric vehicles both in the day ahead and real-time energy trading platforms. Additionally, the PoI smart contract is developed to seamlessly incorporate mathematical optimization with an increased level of security, scalability, throughput, and low confirmation latency of transactions achieved through the reduced effort involved in finding and confirming the optimal solution in comparison with conventional blockchain consensus mechanisms.

This paper proposes an optimal controller and estimator for energy dispatch to balance the power supply and demand considering communication delays. The proposed algorithm involves modeling an autonomous nanogrid (ANG) consisting of distributed energy resources, energy storage systems, loads, an $H_{\infty }$ controller with a reference power modulation technique, and a state estimator. The ANG was developed to express the dynamic supply-demand energy balance of a nanogird system. Reference power modulation was designed to generate the desired ESS power based on the imbalanced energy. Random communication delays were modeled using a stochastic variable satisfying the Bernoulli random binary distribution. The optimal $H_{\infty }$ controller and estimator were developed using a linear matrix inequality approach to exponentially stabilize the closed-loop system. Simulations were performed using real daily demand forecasts obtained from the Korea Meteorological Administration to demonstrate the effectiveness of the proposed real-time optimization algorithm.

As decarbonization policies emerge around the world, research on cost-effective renewable energy is increasingly emphasized, but the uncertainty inherent in renewable energy disrupts the operation of power systems. Considering these issues, this paper presents a peer-to-peer (P2P) transaction platform that minimizes the cost of each transaction participant while considering photovoltaic (PV) generation uncertainty using stochastic programming. First, in the proposed algorithm, P2P energy transactions are conducted between prosumers who own PV and consumers. Then, prosumers purchase uncertainty demand response (UDR) capacity from consumers to counter the uncertainty of their PV. The performance of the proposed platform is verified through a case study using MATLAB 2021b Academic, Optimization Tool toolbox, and Matpower toolbox to demonstrate the economic feasibility by comparing the operating costs of participants and the power flow of Point of common coupling (PCC) with and without UDR transaction. The case studies show that the prosumers reduce the cost damage caused by the PV generation forecast error through the day-ahead UDR transaction, and the consumers reduce operating cost by bidding additional capacity in the UDR transaction that was not matched in the P2P transaction. In addition, the result of the power flow analysis indicates that the subject grid operates closer to day-ahead scheduling when the UDR transactions and implementations are performed.

Developing distributed renewable energy is vital to energy system transformation, while organizing market trading will promote the production and consumption of distributed renewable energy. However, the uncertainty of renewable energy output causes deviations during market delivery, which threats the security of distribution system operation. It is still difficult for existing market-based trading mechanisms to motivate market players to reduce the deviations. Therefore, this paper gives guidance to the honest delivery behaviors of distributed green energy producers by quantifying credit costs. Considering the strategic bidding behaviors of distributed green energy producers, it establishes a market model taking the credit costs into account. Then, it proposed an iterative algorithm based on the optimal response theory to calculate the Nash equilibria of the green energy market. The results of the case study show that the market mechanism proposed can give guidance to the integrity behavior of green energy producers in an incentive-compatible way, reducing the delivery deviation while improving social welfare.

Non-wires alternatives and microgrid technologies are maturing and present great opportunities for electric utilities to increase the benefits they offer to their customers. They have the potential to decrease the cost of resolving traditional electrical system loading issues, contribute to carbon emissions reductions, and improve the electrical distribution system’s resilience to extreme weather events. The authors of this manuscript present a review of the research on microgrids and their practical applications. This is leveraged with the past work of the authors of this manuscript and other authors to develop specific objectives for microgrids, practical criteria for engineers to consider when deploying microgrids, stochastic methods to optimize microgrid designs, and black start requirements. This guidance is then used for the design of actual networked microgrids being deployed with adaptive boundaries.

To integrate large-scale variable renewable energy resources (RESs) in modern power grids, the coordinating control area (CA) operation is the most cost-effective method. This article reviews the technical aspects of CA cooperation. Firstly, a brief overview of the active balancing control within each CA is discussed. Secondly, three general control structures for CA cooperation are innovatively proposed, the corresponding implementation details are analyzed, and some representative technologies are also provided in the systematic analysis. Then, some future research directions such as large-scale power sharing by DC, active power control of RES bases, and the new structure for distributed energy resources in local power grids are prospected. Finally, the changes in power systems brought about by their evolution and importance for further promoting cooperation between CAs are summarized. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The increasing inclusion of electric vehicles (EVs) in distribution systems is a global trend due to their several advantages, such as increased autonomy and reduced price. However, this growth requires a high investment in electric vehicle charging stations (EVCSs) infrastructure to satisfy the demand. Thus, in this paper, an adequate planning of the EVCSs allocation and sizing is carried out to ensure better power quality indices, in addition to reducing costs related to EVCSs installation and EV users' recharging. Besides the optimal planning of EVCSs, this paper performs the optimal allocation and sizing of distributed energy resources (DERs) in order to mitigate the problem related to voltage levels and power losses. Additionally, a spatial distribution of EVs was performed for the 24 h, considering residential and commercial nodes of a distribution feeder test, from the closeness centrality of graph theory. The non-dominated sorting genetic algorithm II (NSGA-II) was applied to obtain the Pareto curve, which made it possible to minimize the objective functions for the IEEE 34-node test feeder. Even with the integration of EV loads, the optimal allocation and sizing of EVCSs and DERs promoted a reduction in voltage deviation and power losses of 11.576% and 56.683%, respectively, in addition to a low variation in the energy costs of 4.447%. [ABSTRACT FROM AUTHOR]

The goal of the paper is twofold: a) following the identification of the merits of Distributed Energy Resources (DERs) by FERC 2222 for the flexibility they bring to the grid, we recognize water heaters and thermostats as mini-batteries and introduce volumetric/swing options that the Aggregator can sell by shifting consumers' consumption while rewarding them; b) We propose to address the pricing complexity of these volumetric options - which are in our setting very general in terms of number of exercises and quantities constraints - by a novel approach that combines Monte Carlo simulations and Recurrent Neural Networks (RNN). [ABSTRACT FROM AUTHOR]

The constant need for fuel to meet the commercial sector’s ever-increasing demand has driven researchers to discover and optimize renewable energy resources, paving the way for sustainable production of reliable and clean energy resources. The goal of the current work is to close the gap in process parameter optimization needed to convert wind energy wake from traffic on highways into electrical energy utilizing vertical-axis wind turbines (VAWTs). The energy output from the VAWT is analyzed to investigate how it is impacted by the variations in multiple parameter settings. Using the central composite design (CCD), a three-level four-factor array was used to investigate the following parameters: VAWT vertical distance (VD) and horizontal distance (HD) as continuous parameters, while road side (S) and location (L) of VAWT as categorical parameters. To find the most important parameter, response surface methodology (RSM) optimization and an analysis of variance (ANOVA) test are performed. L accounts for 66.67% of the total variable, with S coming in second with 51.80%. Using the best results from RSM and ANOVA, a confirmation test is run, and the results show yields of 88.75% ± 0.05% and 87.5% ± 0.05%, respectively. Therefore, RSM and ANOVA can be utilized equally for optimization at the same VAWT design. Lastly, the findings of the economic and environmental evaluation demonstrate that, in comparison to the basic settings, VAWT operating at optimal settings can save up to 180% and 200% more energy and reduce carbon emissions, respectively.

This paper presents a strategy for estimating fault direction in active distribution networks, focusing on the high penetration of distributed energy resources, changing operating conditions, untransposed lines, and unbalanced loads.The proposed strategy introduces a power-based directional criterion derived from analysing active power within three-phase unbalanced systems known for their inherent unidirectional nature. The method enables rapid direction estimation by measuring the difference between pre-fault average active power and during-fault average active power values, utilising two moving windows. Notably, this difference is positive for forward faults and negative for backward faults, offering clear directionality insights. The strategy is validated using a modified IEEE 34-bus test system, where it demonstrates feasibility under several fault scenarios, including all fault types, varying fault impedance, operating conditions, noise levels, and sampling frequencies. Additionally, the proposed algorithm is compared with contemporary and conventional approaches, highlighting its effectiveness.

This paper proposes an analytic hierarchy process (AHP) based optimal load scheduling (AOLS) framework in an islanded distribution network to schedule power among prioritized loads under limited generation conditions. The scheduling problem is solved in two stages: in the first stage, AHP is used to determine the priority ranking of loads by assigning weights that are calculated based on the relative importance of loads and their attributes; in the second stage, optimal load scheduling is implemented by minimizing the total cost of load curtailment, subjected to network constraints and security limits. The scheduling problem is solved in four different cases by varying the total power generation using General Algebraic Modeling System (GAMS) to supply power to the loads in the order of priority. Taking the modified IEEE 34 node distribution network as an example, a simulation study is performed to show that the total cost of load curtailment according to AOLS is 18.51%, 24.86%, 28.65%, and 31.54% less than that of optimal load scheduling framework with equal weightage to all loads (OLSEW) in cases 1, 2, 3 & 4, which verifies the effectiveness of the proposed AOLS framework.

The current electricity system has a large number of carbon emissions. As an environmentally friendly and low-carbon electricity generation source, distributed energy sources gradually get more and more attention. However, the participation of a large number of distributed energy sources brings uncertainty to the operation of the electricity market. Prosumers could effectively avoid the instability caused by the participation of distributed energy sources in the electricity market. At present, the strategy of the prosumer participating in the electricity market has been thoroughly studied. However, equilibrium analysis for prosumer’s participation in joint electricity and carbon markets still needs to be studied in depth. Therefore, we propose a method for the prosumer to participate in the joint electricity and carbon markets, which could effectively reduce carbon emissions. First, we build a carbon market model. Then, the equilibrium model of the prosumer’s participation in joint electricity and carbon markets is constructed. The equilibrium model can realize the equilibrium between the coordination of prosumers and joint markets. Finally, we adopt an iterative algorithm to solve the equilibrium model. Prosumers and joint electricity and carbon markets transmit marginal information to each other. According to the marginal information. The equilibrium between Prosumer and joint markets is solved through iteration by self-optimization and self-decision. Case studies based on the IEEE 30-bus system verify the effectiveness of the model and method proposed in this paper.

The electric vehicle (EV) industry has emerged in response to the necessity of reducing greenhouse gas emissions and combating climate change. However, as the number of EVs increases, EV charging networks are confronted with considerable obstacles pertaining to accessibility, charging time, and the equilibrium between electricity demand and supply. In this paper, we present a techno-economic analysis of EV charging stations (EVCSs) by building type. This analysis is based on public EVCS data and considers both standalone local grid operation and integrated operation of distributed energy resources (DERs) and the local grid. The analysis has significant implications for the management of the electricity grid and the utilization of sustainable energy, and can result in economic benefits for both residential, commercial, and public buildings. The analysis indicates that integrating DERs with the local grid at EV charging stations can reduce local grid usage relative to EV demand. Nevertheless, there are also complexities, such as initial investment and maintenance costs, especially the weather-dependent performance variability of solar, which require financial support mechanisms, such as subsidies or tax incentives. Future research should focus on different DER integrations, regional and seasonal variability, user behavior, installation location, policy and regulatory impacts, and detailed capital expenditure analysis. Such research will advance DER and EVCS integration and contribute to increasing the efficiency and sustainability of urban energy systems.

To integrate large-scale variable renewable energy resources (RESs) in modern power grids, the coordinating control area (CA) operation is the most cost-effective method. This article reviews the technical aspects of CA cooperation. Firstly, a brief overview of the active balancing control within each CA is discussed. Secondly, three general control structures for CA cooperation are innovatively proposed, the corresponding implementation details are analyzed, and some representative technologies are also provided in the systematic analysis. Then, some future research directions such as large-scale power sharing by DC, active power control of RES bases, and the new structure for distributed energy resources in local power grids are prospected. Finally, the changes in power systems brought about by their evolution and importance for further promoting cooperation between CAs are summarized.

Recently, the generalization of P2P (peer-to-peer) technology with enhanced security due to blockchain technology and the expansion of renewable energy-based distributed energy resources have led to blockchain technology being applied in power transactions, thus giving the potential to become a new platform for DC microgrid operation. Meanwhile, the voltage of a DC microgrid represents the balance of energy supply and demand and also serves as a stability index. The balance is represented as a steady state; the stability is represented during and after events. This paper examines the stability of the DC microgrid built on a university campus in Korea and, in particular, the blockchain technology-based power transactions performed in the DC microgrid. The test is based on the pre-planned transaction schedule applied in the DC microgrid. The transaction schedule has used day-ahead and real-time bidding data. Although many technologies are included in the project, this paper focuses on the voltage stability of the DC microgrid. In addition, the DC protection is applied and evaluated. To consider general DC protection, the DC breaker was simplified with several IGBTs, diodes, capacitors, and arrestors and was designed to interrupt the fault current within five milliseconds. The stability was evaluated using a PSCAD/EMTDCTM. [ABSTRACT FROM AUTHOR]

In this paper, a multi-objective optimization approach to solve the problem of optimal allocation and sizing of inverter-based distributed energy resources (DERs) in distribution systems is presented. The objectives of this allocation and sizing problem consist of the minimization of the investment and operation costs, the voltage deviation and the short-circuit currents. The recloser–fuse coordination constraints were included in the mathematical formulation of the problem, in order to preserve the original network protection scheme. It is important to mention that this protection scheme was also carried out in this paper, using the multi-objective approach, to minimize the operating time of the reclosers and fuses. The distribution system considered to evaluate the proposed methodology was the IEEE 34-Node Test Feeder, and the non-dominated sorting genetic algorithm II was used to solve the proposed multi-objective problems. Furthermore, a time-series-based probabilistic approach, through the Monte Carlo simulation, was adopted to deal with the uncertainties of the load and power generated by each DER. Finally, from the results, it was possible to reduce the investment and operation costs by 15.61% when compared to the system without DERs, improve the voltage profile and preserve the original protection scheme present in the network. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are a promising solution to resolve the greenhouse emission issue as well as the fossil fuels scarcity problem in the future. However, as the number of EVs significantly increases, the excessive charging demand of EV parking lots (EVPLs) in the charging station may incur an instability problem to the electricity network. In this paper, the optimal allocation and sizing of EVPLs were carried out in order to minimize the costs of the EVPLs installation and EV users' distance traveled and recharge. As a consequence, it encourages the participation of vehicle users in a demand response program related to smart charging and time of use tariffs. It is important to mention that the increasing inclusion of EVs negatively affects the voltage level and power losses of the distribution system. Therefore, this paper also performs the optimal allocation and sizing of distributed energy resources (DERs) in order to mitigate these problems. The non-dominated sorting genetic algorithm II (NSGA-II) was applied to obtain the Pareto curve, which made it possible to minimize the objective functions for the IEEE 33-node test feeder and 25-node transportation network. Furthermore, it was considered a spatial distribution of EVs for the 24-h from the closeness centrality of graph theory. The optimal planning of EVPLs and DERs promoted a 35%, 23.14% and 34.11% reduction of the travel distance, voltage deviation and power losses, respectively. [ABSTRACT FROM AUTHOR]

To solve the overload problem caused by the high proportion of renewable energy into the power system, it is particularly important to find a suitable distribution network planning scheme. Existing studies have effectively reduced the planning cost by incorporating virtual power plants into the distribution planning process, but there is no quantitative analysis of the flexible resources inside the virtual power plant. At the same time, the traditional planning process does not pay much attention to the acquisition of photovoltaic and load data. Therefore, in this paper, we propose a distribution network planning method considering the flexibility of distributed energy resources in virtual power plants. Firstly, taking the distribution network planning including the virtual power plant as the research object, the flexibility of the distributed energy resource of the virtual power plant was quantified. Then, in order to achieve the goal of minimizing the operating cost of system planning, a distribution network planning model considering the flexibility of distributed energy resources in the virtual power plant is established. In this model, the impact of virtual power plants flexibility on the distribution network planning process is mainly considered. Secondly, this paper uses the improved k-means clustering algorithm to obtain the typical data of PV and load. The algorithm effectively overcomes the impact of PV and load output fluctuations on the planning process. Finally, the simulation results show that the proposed planning model can effectively reduce the operation cost of system planning by using distributed energy storage system and distributed energy resource flexibility. At the same time, the PV absorption rate of the PV power station inside the distribution network is improved. [ABSTRACT FROM AUTHOR]

The future power system will be characterized by many small decentralized power plants—so-called distributed energy resources (DERs). The integration of these DERs is vital from an economic and grid operation point of view. One approach to this is the aggregation of such DERs. The formation of coalitions as an aggregation method has already been examined in the literature and applied in virtual power plants, active distribution networks, and microgrids. The spread of DERs also increases the need for flexibility and dynamics in the power grid. One approach to address this can be overlapping coalitions. Therefore, in this paper, we first performed an analysis of related work and, in this context, found no work on overlapping coalitions for energy use cases in the literature. We then described a method for dynamic coalition formation, called dynamic coalition in electricity markets (DYCE), and analyzed how DYCE would need to be extended to include overlapping coalition formation. The extension includes the phases of product portfolio optimization and the actual coalition formation. Our analysis of DYCE shows that the methods used for the optimization of the DYCE sub-tasks are not suitable for overlapping coalitions and would have to be replaced by other methods in order to be able to form overlapping coalitions. [ABSTRACT FROM AUTHOR]

As the adoption of distributed energy resources (DERs) grows, the future of electricity distribution systems is confronted with significant challenges. These challenges arise from the transformation of consumers into prosumers and the resulting increased system complexity, leading to more pressure on the distribution grids. To address this complexity, a Digital Twin framework is designed to simulate DERs within distribution grids effectively. This framework is structured around four key modules: DERs, the electricity distribution grid, the energy management system, and the consumers. It incorporates a communication interface to facilitate interactions among these modules and includes considerations for grid topologies and demand-side configurations. The framework allows for the exploration of various DER adoption rates and capacities. The validation of this framework involves case studies on two Danish distribution grids with scenarios incorporating rooftop photovoltaic (PV) systems, batteries, and electric vehicles, considering different combinations of these technologies. The findings demonstrate the framework’s ability to depict the states of the grid, PV systems, electric vehicles, and battery systems with a 10 min resolution over periods ranging from a day to over a decade.

This article discusses the key technologies of smart energy system optimization. It highlights the importance of constructing and optimizing a smart energy system that combines energy and information technology to meet the requirements of the rapidly changing energy field. The article presents four research articles that focus on topics such as economic energy management, load forecasting, time-of-sale price adjustment, and grid regulation. These research articles propose strategies and models to achieve efficient and economical operation, improve the robustness of the system, and optimize the control operation of the power grid. The article emphasizes the significance of the construction and optimization of smart energy systems for the long-term development of the energy field. [Extracted from the article]

Recent years have seen a rapid uptake in distributed energy resources (DER). Such technologies pose a number of challenges to network operators, which ultimately can limit the amount of rooftop solar photovoltaic (PV) systems that can be connected to a network. The objective of this industry-based research was to determine the potential network effects of forecast levels of customer-owned rooftop solar PV on Energy Queensland's distribution network and formulate functions that can be used to determine such effects without the requirement for detailed network modeling and analysis. In this research, many of Energy Queensland's distribution feeders were modeled using DIgSILENT PowerFactory and analyzed with forecast levels of solar PV and customer load. Python scripts were used to automate this process, and quasi-dynamic simulation (QDSL) models were used to represent the dynamic volt–watt and volt–var response of inverters, as mandated by the Australian Standard AS/NZS 4777. In analyzing the results, linear relationships were revealed between the number of PV systems on a feeder and various network characteristics. Regression was used to form trend equations that represent the linear relationships for each scenario analyzed. The trend equations provide a way of approximating network characteristics for other feeders under various levels of customer-owned rooftop solar PV without the need for detailed modeling. [ABSTRACT FROM AUTHOR]

With the growing penetration of distributed energy resources (DER), the accompanying challenges have led utilities to limit the hosting capacities of DER installations on distribution feeders. A distribution static synchronous compensator (DSTATCOM) is a power electronic device to provide dynamic injections and absorption of reactive power into the distribution grid with more flexible and reliable voltage control and power quality improvement. A distributed energy resources management system (DERMS) is developed to provide more effective control of a DSTATCOM that can help substantially increase hosting capacity and mitigate overvoltage problems with the existing feeder. A Taiwan power company's (Taipower) feeder is selected for computer simulation, and the DSTATCOM is employed in the test feeder to demonstrate the effectiveness of the DSTATCOM in improving the overvoltage problems. The voltage/reactive power (Volt/VAR) control of the DSTATCOM helps reduce overvoltage/voltage fluctuations as the DER output increases. [ABSTRACT FROM AUTHOR]

In the advent of recent developments in a higher efficiency Direct Current (DC) system, the DC microgrid proves to be a permissible future to match the load demand with distributed power generation. The distribution of the DC power system benefits more than the Alternate Current (AC) power system, and due to recent developments, many researchers focus on DC microgrid. DC microgrid system has the advantage of direct interfacing with Renewable energy sources (RES) and energy storage systems (ESS), making the system highly efficient and distinct. Since the protective devices used in AC power systems do not support DC applications, there are challenges in designing and implementing a proper protection device for DC microgrid, which includes the breaking of fault current and the rapid response in handling fault current, capability to overcome the fault and cost of implementing. Unlike traditional circuit breakers that work with a fixed voltage source, Z-Source Breakers are capable of interrupting current in systems with variable voltage sources, making it appropriate for applications involving renewable energy sources, microgrids and energy storage systems. These breakers use a unique Z-Source network to provide enhanced flexibility and reliability in managing electrical faults in modern power grids. This study intends to analyses the recent growth in the impedance-based DC circuit breaker and DC microgrid’s fault characteristics.

In the past decade, substantial investments have been made in researching and developing concepts and technologies to support the smart grid, renewable integration, and grid-interactive buildings. Adaptation of integrated solar photovoltaics with energy storage is increasing in residential buildings as consumers and utilities are becoming aware of their economic benefits and resilience benefits. Effective integration and control of these systems with other building loads is critical for providing load flexibility to improve building energy efficiency, reduce carbon footprint, and support grid resiliency. In recent years vendors are shifting towards device-level optimization and defining more sophisticated operational modes for controlling energy storage systems rather than charge and discharge power. As a result, optimization techniques must encompass the characteristics of these modes and their interactions with other system disruptions and attributes. This complexity gives rise to a nonlinear optimization problem that cannot be effectively addressed by an open-source solver and is impractical to implement in real-world scenarios. In this paper, we designed and evaluated a linear multi-objective model-predictive control optimization strategy for integrated photovoltaic and energy storage systems in residential buildings by using manufacturer-defined operational modes. The optimization goal is to minimize the power-purchasing cost from the grid and maximize the power selling cost to the grid. We developed a generalized method to keep the optimization linearized, even with operational modes consideration while coupling the modes with the overall system charging and discharging power. Our simulation results were aligned with real-world measurements and validated the linearized optimization formulation for each operational mode and for the economic use-case. The optimization results for the economic use-case demonstrated that the power associated to grid charge is mostly larger than the grid discharge power which means the optimization tried to maximize the power selling to the grid when the price is high and avoid power purchasing from the grid during high price.

Several challenges are encountered while integrating microgrids (MGs) with an existing utility grid. These are low inertia, intermittent nature of renewable energy resources (RESs), sensor and actuator faults, unbalanced and nonlinear loads, supply-demand mismatch, and uncertain switching functions of the power electronic converter. MG control is fully dependent on the communication network, which is also susceptible to different types of failures such as noise in communication links, communication delay, limited bandwidth, packet dropout, and various malicious cyber attacks. Very few papers are available in the literature that focus on the review of control schemes for MG. Consequently, this paper presents a comprehensive review of different robust and adaptive control schemes that address the challenges encountered due to communication constraints, uncertainties, and disturbances for different MG topologies such as AC, DC, and hybrid AC/DC MGs, as well as the current research trends in the field of robust and adaptive control. It can be concluded that to achieve control objectives of MG and overcome the existing challenges, robust and adaptive controllers show significantly improved performance in terms of transient and steady-state behavior and robustness as compared to traditional controllers.

Microgrids consisting of renewable energy based distributed generators have become popular as a way of energizing off-grid systems. Due to their low-inertia, these distributed generators require a robust frequency control system to maintain it. This problem is amplified by the fact that there is no centralized grid that anchor the system voltage and frequency. For this reason, such off-grid microgrid employs storage systems and diesel generators to provide some flexibility. Flywheel energy storage systems (FESSs) have very quick reaction time and can provide frequency support in case of deviations. To this end, this paper develops and presents a microgrid frequency control system with FESS. The system performance tests are performed with real-equipment where FESS is connected to digital real time simulator. Several frequency events are triggered and the frequency profile is observed to compare microgrid’s performance with and without the developed frequency control. Results show that flywheel energy storage system provides a much smoother frequency profile with less deviations.

Due to climate changes happening in the past few years, the necessity for the integration of renewable energy sources and other low-carbon technologies is ever-growing. With the integration of low-carbon technologies, the power system is facing changes, that are particularly visible in distribution systems. A high share of distributed energy resources installed at the medium voltage or low-voltage level creates new challenges for Distribution System Operators. To overcome challenges, Distribution System Operators need to look beyond traditional methods and find new ones that will help in the mitigation of potential problems in the planning and operation of distribution networks. Recent research efforts have put the attention on using machine learning based algorithms and methods. Machine learning methods have shown great potential in the prediction of consumption and production, scheduling of flexibility services, near real-time operations, etc. To summarize their advantages, but also shortcomings, a comprehensive review of using machine learning based methods and algorithms in the planning and operation of smart, active distribution systems is provided. In addition to the already developed and presented applications, we identify the current research gap and proposed future research directions with machine learning applications.

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)