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In order to solve the problem that allocating the whole virtual power station is not centralized and promoting the free access of power distribution, a research on distributed scheduling of urban power network based on blockchain technology is proposed. and a scheme is proposed. Based on the consistency of decentralized virtual power plant and blockchain in decentralized point-to-point interaction and decentralized cooperation, this paper proposes to use blockchain consensus mechanism to realize distributed scheduling of virtual power plant. According to the principle of continuous micro- increment cost, the optimal economic dispatch of virtual power system is realized by taking the micro- increment characteristic as the variable. and the optimal economic dispatch strategy is realized. As a node of the blockchain, each distributed energy in the virtual power plant has a complete backup of the key data of the whole network. When the load changes, each node uses the PBFT consensus algorithm to independently calculate the new power of each unit. The new power data is stored on the chain, and the global consistency of micro incremental features is maintained to realize the reasonable load distribution among units. The experimental results show that, when the initial t = 1, the λ the values of each unit are different. The system does not meet the principle of constant consumption micro-increase rate, and the system does not operate in the optimal state. After a PBFT consensus, λ variable reaches the same value at t=8, and the system reaches the optimal operating state. Conclusion: The simulation experiment verifies the effectiveness of the algorithm, realizes distributed scheduling by using blockchain, and provides a feasible reference scheme for the operation mode of decentralized virtual power plant. [ABSTRACT FROM AUTHOR]

A virtual power plant (VPP) has the ability to aggregate numerous decentralized distributed energy resources using advanced control technology, offering a promising approach for low-carbon development. In order to enhance the VPP's contribution to reducing carbon emissions, a bi-level framework is proposed that incorporates an integrated energy-carbon price response mechanism. This model allows VPPs to participate in a multi-energy system through a multi-agent Stackelberg game framework. Initially, a transaction model is established where the power distribution system operator and the gas distribution system operator act as leaders, while the virtual power plant operator acts as a follower in the multi-energy system. Subsequently, an integrated energy-carbon pricing method, rooted in carbon emission flow theory, is introduced to encourage VPPs to proactively adjust their energy-use and trading strategies within multi-energy systems, thereby promoting multiprincipal interactive trading. To achieve a distributed solution among multiple entities while maintaining the privacy of each entity's information, the adaptive step-size alternating direction multiplier method is employed. The feasibility and effectiveness of the proposed model and method are then demonstrated through case studies. [ABSTRACT FROM AUTHOR]

Aiming at the problems of small thermal power units and biomass mixed combustion units with small generation loads and insufficient primary frequency modulation capability, which cannot be connected to the virtual power plant, this paper adopts a variety of flexibility retrofit methods for the units and explores the peak load capability of the units. Then, multiple units are coupled, and the unit coupling scheme with better economy and environmental protection is screened using comprehensive evaluation indexes. While evaluating the peaking load space of multiple unit coupling, the units' primary frequency regulation capability and new energy consumption capability are improved. According to the calculation results, the low-pressure cylinder zero-output retrofit has the largest peaking potential among different technical paths, in which unit #3 has 27.55 MW of peaking space. The compression heat pump decoupling retrofit has the best economy, in which the daily profit of unit #3 increases from 0.93 to 1.02 million CNY with an increase of 0.09 million CNY. After the unit has been retrofitted with steam extraction, the three units can be coupled to meet the national feed-in standards. The multiple unit coupling can accommodate up to 203.44 MW of other energy sources while meeting the standard. [ABSTRACT FROM AUTHOR]

To optimize energy structure and efficiently utilize renewable energy sources, it is necessary to establish a new electrical power–gas mutual transformation virtual power plant that has low-carbon benefits. To promote the economic and low-carbon operation of a virtual power plant and reduce uncertainty regarding the use of new energy, a multi-timescale (day-ahead to intraday) optimal scheduling model is proposed. First, a basic model of a new interconnected power–gas virtual power plant (power-to-gas demand response virtual power plant, PD-VPP) was established with P2G and comprehensive demand response as the main body. Second, in response to the high volatility of new energy, a day-ahead to intraday multi-timescale collaborative operation optimization model is proposed. In the day-ahead optimization period, the next day's internal electricity price is formulated, and the price-based demand response load is regulated in advance so as to ensure profit maximization for the virtual power plant. Based on the results of day-ahead modeling, intraday optimization was performed on the output of each distributed unit, considering the cost of the carbon emission reductions to achieve low-carbon economic dispatch with minimal operating costs. Finally, several operation scenarios are established for a simulation case analysis. The validity of the proposed model was verified via comparison. [ABSTRACT FROM AUTHOR]

Virtual power plants (VPPs) aggregate a large number of distributed energy resources (DERs) through IoT technology to provide flexibility to the grid. It is an effective means to promote the utilization of renewable energy, and enable carbon neutrality for future power systems. This paper addresses the evaluation issue of DERs' low-carbon benefits, proposes a flexibility assessment model for self-organized VPP to quantify the low-carbon value of DERs' response behavior in different time periods. Firstly, we introduce the definition of zero-carbon index based on the curve simultaneous rate of renewable energy and load demand. Then, we establish a multi-level self-organized aggregation method for virtual power plants, define the basic rules of DER, and characterize its self-organized aggregation as a Markov game process. Moreover, we use QMIX to achieve a bottom-up, hierarchical construction of VPP from simple to complex. Experimental results show that when users track the zero-carbon curve, they can achieve zero carbon emissions without reducing the overall load, significantly enhancing the grid's regulation capabilities and the consumption of renewable energy. Additionally, self-organized algorithms can optimize the combinations of DERs to improve the coordination efficiency of VPPs in complex environments. [ABSTRACT FROM AUTHOR]

As the scale of units within virtual power plants (VPPs) continues to expand, establishing an effective operational game model for these internal units has become a pressing issue for enhancing management and operations. This paper integrates photovoltaic generation, wind power, energy storage, and constant-temperature responsive loads, and it also considers micro gas turbines as auxiliary units, collectively forming a typical VPP case study. An operational optimization model was developed for the VPP control center and the micro gas turbines, and the game relationship between them was analyzed. A Stackelberg game model between the VPP control center and the micro gas turbines was proposed. Lastly, an improved D3QN (Dueling Double Deep Q-network) algorithm was employed to compute the VPP's optimal operational strategy based on Stackelberg game theory. The results demonstrate that the proposed model can balance the energy complementarity between the VPP control center and the micro gas turbines, thereby enhancing the overall economic efficiency of operations. [ABSTRACT FROM AUTHOR]

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Under the guidance of the "dual-carbon" target, the utilization of and demand for renewable energy have been growing rapidly. In order to achieve the complementary advantages of renewable energy in virtual power plants with different load characteristics and improve the rate of consumption, an interactive operation strategy for virtual power plants based on asymmetric Nash negotiation is proposed. Firstly, the photovoltaic virtual power plant is proposed to establish the optimal scheduling model for the operation of the virtual power plant, and then the asymmetric Nash negotiation method is adopted to achieve the fair distribution of benefits. Finally, the ADMM distribution is used to solve the proposed model in the solution algorithm. The simulation results show that the revenue enhancement rates are 28.27%, 1.09%, and 12.37%, respectively. The participating subjects' revenues are effectively enhanced through P2P power sharing. Each subject can obtain a fair distribution of benefits according to the size of its power contribution, which effectively improves the enthusiasm of the PV virtual power plant to participate in P2P interactions and thus promotes the development and consumption of renewable energy. [ABSTRACT FROM AUTHOR]

In adapting to the double-high development trend of high-voltage direct current (HVDC) receiving-end power systems and solving optimization problems in emergency frequency control (EFC) supporting virtual power plants (VPPs) in large-scale power systems, a parameter estimation method for a VPP frequency response model based on a successive linear programming (SLP) method is proposed. First, a "centralized/decentralized" hierarchical control architecture for VPP participation in EFC is designed. Second, the frequency response characteristics of multiple flexible resources are scientifically analyzed, and the system frequency response (SFR) model and equivalent model of VPP are constructed. Subsequently, parameter estimation of the VPP frequency response model is carried out based on the SLP method, aiming to balance the accuracy and computational efficiency of the model. Finally, the effectiveness of the proposed methodology is verified by using PSD-BPA to simulate and test the three-zone HVDC recipient area grid. [ABSTRACT FROM AUTHOR]

Under the background of uncertainty of renewable energy output, in order to solve the coordination of interests between electric vehicle charging station and virtual power plant, a bi-level optimal dispatching model was constructed with virtual power plant operator as the main body and electric vehicle(EV) charging station as the subordinate body. Firstly, combining the hydrogen energy system and the demand response characteristics of electric and heat load, a source-load coordinated response mechanism was designed to improve the overall flexible operation of the system. Secondly, a dynamic pricing strategy was proposed by combining the energy-load distribution of virtual power plant, carbon emissions, and the operation plan of EV charging station to guide the lower EV charging station to participate in virtual power plant scheduling. It ensured that it can prioritize the consumption of renewable energy and coordinate the interests between the upper and lower levels. Thirdly, a stepped carbon trading mechanism and carbon capture equipment were introduced to further enhance the system's low-carbon operation capability. Finally, the CPLEX commercial solver was used to solve the bi-level planning model iteratively, and the joint optimal discrimination mechanism was introduced to select the joint optimal solution. The results verify the effectiveness of the proposed model and method in coordinating the interests of different stakeholders and reducing the carbon emissions of the system [ABSTRACT FROM AUTHOR]

Introduction: In the context of the evolving energy landscape, the efficient integration of energy storage systems (ESS) has become essential for optimizing power system operation and accommodating renewable energy sources. Methods: This study introduces LoadNet, an innovative approach that combines the fusion of Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) models, along with a self-attention mechanism, to address the challenges associated with ESS integration in power system operation. LoadNet aims to enhance the management and utilization of ESS by effectively capturing the complex temporal dependencies present in time-series data. The fusion architecture of TCN-GRU in LoadNet enables the modeling of both short-term and long-term dependencies, allowing for accurate representation of dynamic power system behaviors. Additionally, the incorporation of a self-attention mechanism enables LoadNet to focus on relevant information, facilitating informed decision-making for optimal ESS operation. To assess the efficacy of LoadNet, comprehensive experiments were conducted using real-world power system datasets. Results and Discussion: The results demonstrate that LoadNet significantly improves the efficiency and reliability of power system operation with ESS. By effectively managing the integration of ESS, LoadNet enhances grid stability and reliability, while promoting the seamless integration of renewable energy sources. This contributes to the development of a more sustainable and resilient power system. The proposed LoadNet model represents a significant advancement in power system management. Its ability to optimize power system operation by integrating ESS using the TCN-GRU fusion and selfattention mechanism holds great promise for future power system planning and operation. Ultimately, LoadNet can pave the way for a more sustainable and efficient power grid, supporting the transition to a clean and renewable energy future. [ABSTRACT FROM AUTHOR]

This paper develops a distributed cooperative optimization model for multiple virtual power plant (VPP) operations based on multi-stage robust optimization and proposes a distributed solution methodology based on the combination of the alternating direction method of multipliers (ADMMs) and column-and-constraint generation (CCG) algorithm to solve the corresponding optimization problem. Firstly, considering the peer-to-peer (P2P) electricity transactions among multiple VPPs, a deterministic cooperative optimal operation model of multiple VPPs based on Nash bargaining is constructed. Secondly, considering the uncertainties of photovoltaic generation and load demand, as well as the non-anticipativity of real-time scheduling of VPPs in engineering, a cooperative optimal operation model of multiple VPPs based on multi-stage robust optimization is then constructed. Thirdly, the constructed model is solved using a distributed solution methodology based on the combination of the ADMM and CCG algorithms. Finally, a case study is solved. The case study results show that the proposed method can realize the optimal scheduling of renewable energy in a more extensive range, which contributes to the promotion of the local consumption of renewable energy and the improvement of the renewable energy utilization efficiency of VPPs. Compared with the traditional deterministic cooperative optimal operation method of multiple VPPs, the proposed method is more resistant to the risk of the uncertainties of renewable energy and load demand and conforms to the non-anticipativity of real-time scheduling of VPPs in engineering. In summary, the presented works strike a balance between the operational robustness and operational economy of VPPs. In addition, under the presented works, there is no need for each VPP to divulge personal private data such as photovoltaic generation and load demand to other VPPs, so the security privacy protection of each VPP can be achieved. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. 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.)

In this paper, virtual power plant (VPP) planning is done using distributed generation sources to create a safe platform for electricity exchange and to increase the profitability and sustainability of electricity. In the proposed model, the effect of micro-grid interaction with the electricity market in the presence of distributed generation resources and storage is investigated. To solve this problem, an improved artificial bee colony algorithm using the accept-reject method (AR-ABC) is used. The AR method is employed to limit the initial search space as well as for the scenario reduction process. Also, uncertainties related to loads and renewable sources are formulated in a sample micro-grid including micro-turbine (MT), fuel cell (FC), wind turbine (WT), photovoltaic cells (PV) and batteries for storage; the results are compared with those of other methods, which shows this method works better than others. The software simulations of this research are done in the MATLAB software environment. [ABSTRACT FROM AUTHOR]

The virtual power plant (VPP) has been recognized as an effective way to facilitate penetration of renewable and distributed energy resources in electricity markets. This paper introduces an adaptive curtailment strategy for a VPP comprising a wind power plant and uncertain demand, and explores the economic advantage of adaptively adjusting wind power curtailment. A two-stage stochastic model is proposed to deal with the uncertainties in wind power generation (WPG), load demand and market prices. In the model, the bid decision is made in the face of uncertainties in the first stage, while the control (curtailment) decision is made based on realized uncertain parameters in the second stage. This paper provides the closed-form optimal curtailment decision and characterizes the optimal bid decision. An efficient binary search algorithm is developed for optimizing the bid decision. By using a distribution-free approach, we show that as the prediction accuracy of WPG improves, the optimal bid decision converges toward the expected minimal power exchange, leading to a decrease in expected operational cost with diminishing marginal return. Numerical experiments based on real-world data demonstrate that compared with the existing greedy strategy and coordinated strategy, the proposed model can decrease the expected operational cost up to 16.9% and 11.0%, respectively. [ABSTRACT FROM AUTHOR]

Abstract A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy.

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.

Abstract In the context of a low‐carbon economy, considering the output uncertainties of the renewable energy sources and the fluctuation of electrical price, the cost–benefit analysis is proposed for multi‐investor virtual power plant (VPP) under different risk preferences, and multi‐investor VPP capacity sizing problem is researched. First, the structural organization and flexible regulation characteristics of multi‐investor VPPs are analyzed. Second, integrated model and cost–benefit analysis indexes of VPP are built. Third, an optimal capacity sizing model of multi‐investor VPP is established, aiming at income maximization. Game theory is used to configure the distributed generators in the VPP under various cooperative modes and noncooperative modes of multiple investors. Fourth, according to the installed proportion of each distributed generator and the risk preference coefficient of each investor, the income allocation correction model based on the improved Shapley value method is proposed to stabilize the optimal VPP alliance. Finally, the demonstration project in the animation industry park of Sino‐Singapore Tianjin Eco‐City is employed as representative scenarios, which is used to validate the effectiveness of the proposed model. The results prove that the method proposed in this paper can guide knowledge for the investors with different risk preferences when planning the optimal capacity of multiple distributed generators in the VPP.

This paper aims to design and integrate virtual power plant (VPP) into a geographically dispersed production-distribution network for attaining environmental sustainability goal. The VPP is comprised wind turbines, photovoltaics, combined heat and power, and energy storage units. The proposed VPP-based supply chain design model makes strategic allocations on renewable capacity as well as scheduling production, inventory, transportation, and energy generation and trading decisions. The model represents a first-of-its-kind in planning a multi-layer supply chain network in which renewable-based electric-thermal cogeneration is coordinated in production-climate nexus. The study yields the following findings. First, products with larger energy use opt to be made in locations with stronger sunshine or wind profile despite higher transportation costs. Second, the time-of-use rate greatly stimulates the installation of onsite photovoltaics and energy storage. Third, under flat utility rate, reducing energy storage cost has limited benefit to the cost savings of the supply chain operations. [ABSTRACT FROM AUTHOR]

A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy. [ABSTRACT FROM AUTHOR]

To fully explore the potential low-carbon and economic advantages of a virtual power plant (VPP) that aggregates multiple distributed resources, the paper proposes a VPP scheduling model that considers the carbon emission flow (CEF) and demand response (DR), which is characterized by electro-carbon coupling and source-load interaction. First, the electric-carbon characteristics of each distributed resource under VPP are modeled, and the source-load electric-carbon coupling characteristic model is modeled through the CEF theory. On this basis, a load-side multi-type DR model is established to achieve the purpose of source-load synergy to reduce carbon emissions from VPP. To this end, a two-stage scheduling model of VPP considering the source- load electro-carbon coupling relationship is established, and the implementation of the model can reduce power generation costs, carbon emissions and promote clean energy, and the simulation results of the improved IEEE-14 node system verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

An efficient and reliable management system for a cluster of distributed energy resources (DERs) is essential for the sustainable and cost-effective peak management (PM) operation of the power grid. The virtual power plant (VPP) provides an efficient way to manage a variety of DERs for the PM process. This paper proposes a VPP framework for PM of local distribution companies by optimizing the self-scheduling of available resources, considering uncertainties and constraints. The study examines two separate scenarios and introduces novel algorithms for determining threshold values in each scenario. An approach is suggested for the transaction between VPP and the aggregator models. The proposed technique intends to determine the optimal amount of capacity that aggregators can allocate for the day-ahead PM procedure while accounting for both thermostatically controlled and non-thermostatically controlled loads. The proposed VPP framework shows promising results for reducing demand charges and optimizing energy resources for PM. [ABSTRACT FROM AUTHOR]

To tackle the variability of distributed renewable energy (DRE) and the timing differences in load demand, this paper perfects the integrated layout of "source-load-storage" energy control in virtual power plants (VPPs). Introducing a comprehensive control approach for VPPs of varying ownerships, and encompassing load aggregators (LAs), a robust and cost-efficient operation strategy is proposed for VPP clusters. Initially, the influence of real-time electricity prices on cluster energy utilization is taken into account. Flexible shared electricity prices are formulated cluster-wide, based on the buying and selling data reported by each VPP, and are distributed equitably across the cluster. Following this, a flexible supply and demand response mechanism is established. With the goal of minimizing operational costs, this strategy responds to demand (DR) on the end-user side, instituting shifts and reductions in electricity and heat loads based on electricity and heat load forecasting data. On the supply side, optimization strategies are developed for gas turbines, residual heat boilers, and ground-source heat pumps to restrict power output, thus achieving economical and low-carbon cluster operations. Finally, the efficacy of the proposed optimization strategy is demonstrated through tackling numerous scenario comparisons. The results showcase that the proposed strategy diminishes operational costs and carbon emissions within the cluster by 11.7% and 5.29%, respectively, correlating to the unoptimized scenario. [ABSTRACT FROM AUTHOR]

Copyright of Integrated Intelligent Energy is the property of Editorial Department of Integrated Intelligent Energy 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.)

Copyright of Integrated Intelligent Energy is the property of Editorial Department of Integrated Intelligent Energy 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.)

Low carbon operation of power systems is a key way to achieve the goal of energy power carbon peaking and carbon neutrality. In order to promote the low carbon transition of energy and power and the coordinated and optimized operation of distributed energy sources in virtual power plants (VPP), this paper proposes a framework for collaborative utilization of pumped storage–carbon capture–power-to-gas (P2G) technologies. It also constructs a multi-time scale low carbon economic dispatch model for VPP to minimize the internal resource operation cost of VPP in each time period. During the intraday scheduling stage, the day-ahead scheduling results as the planned output and the energy flow is then dynamically corrected at a short-term resolution in the framework. This allows for the exploration of the low-carbon potential of each aggregation unit within the virtual power plant. The results of the simulation indicate that the strategy and model proposed in this paper can effectively encourage the consumption of renewable energy sources, promote the low-carbon operation of power system power, and serve as a valuable reference for the low-carbon economic operation of the power system. [ABSTRACT FROM AUTHOR]

In order to achieve sustainable development, China has proposed to "strive to peak carbon dioxide emissions by 2030 and strive to achieve carbon neutrality by 2060". Virtual power plants (VPPs) are an effective means to achieve carbon neutrality goals. In order to improve the economy and low-carbon performance of virtual power plants, this paper proposes a low-carbon economic optimization dispatching model considering the combined operation of oxygen-enriched combustion (OEC) and electricity-to-ammonia (P2A). Firstly, the mechanism of the combined operation of OEC and P2A is proposed. The oxygen-enriched combustion technology can reduce the carbon emissions of the system and enhance the flexibility of the system operation; P2A can effectively consume renewable energy and improve the energy utilization rate. The by-product of the P2A process, oxygen, is the raw material needed for oxygen-enriched combustion, which reflects the complementary nature of the OEC and P2A.Then, an optimal dispatching model is established with the objective function of minimizing the total cost. Finally, the validity of the proposed model is verified by comparing and analyzing the simulation results of five different models. After the introduction of the combined operation of OEC and P2A, the total cost of the system decreases by 10.95%, and the carbon emission decreases by 34.79%. [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]

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.)

A novel multi-stage time scale economic dispatch scheme is proposed for virtual power plants, taking into account the uncertainties arising from the connection of distribution network sources. This research introduces specific scheduling schemes tailored to various time scales within distribution networks, including a fuzzy optimized day ahead scheduling scheme, an intra-day scheduling scheme combined with Deep Q Network, and an adaptive optimized real-time scheduling scheme. This plan mainly considers the impact of photovoltaic output and conducts scheduling one day in advance through fuzzy optimization. In the intraday scheduling, different strategies were adopted in the study. By combining with Deep Q Network, research on scheduling for intraday demand within the power system. The analysis is conducted through rigorous modeling. Experimental tests were conducted to evaluate the performance of the proposed schemes. The day ahead dispatching primarily considers the impact of photovoltaic output and calculates the cost associated with each link in the grid under three different meteorological conditions. In the intra-day scheduling, the total costs for Scenario 1, Scenario 2, and Scenario 3 are found to be 34,724.5 yuan, 36,296.5 yuan, and 33,275.8 yuan, respectively. Notably, strategies 1 and 2 demonstrate lower costs compared to the pre-day scheduling, with the exception of Scenario 3. In real-time scheduling, considering the matching between sources and sources, the matching rate between sources and sources can be maintained at over 95%, and the stability and cost of the power grid have significantly decreased. In summary, by proposing a multi-stage time scale economic scheduling scheme, this study fully considers the uncertainty of the power supply of the distribution network access, as well as the different needs of day, day and real-time scheduling, providing an effective solution for the power dispatching of virtual power plants and providing important technical support for the reliability and economy of the power system. [ABSTRACT FROM AUTHOR]

In the context of a low‐carbon economy, considering the output uncertainties of the renewable energy sources and the fluctuation of electrical price, the cost–benefit analysis is proposed for multi‐investor virtual power plant (VPP) under different risk preferences, and multi‐investor VPP capacity sizing problem is researched. First, the structural organization and flexible regulation characteristics of multi‐investor VPPs are analyzed. Second, integrated model and cost–benefit analysis indexes of VPP are built. Third, an optimal capacity sizing model of multi‐investor VPP is established, aiming at income maximization. Game theory is used to configure the distributed generators in the VPP under various cooperative modes and noncooperative modes of multiple investors. Fourth, according to the installed proportion of each distributed generator and the risk preference coefficient of each investor, the income allocation correction model based on the improved Shapley value method is proposed to stabilize the optimal VPP alliance. Finally, the demonstration project in the animation industry park of Sino‐Singapore Tianjin Eco‐City is employed as representative scenarios, which is used to validate the effectiveness of the proposed model. The results prove that the method proposed in this paper can guide knowledge for the investors with different risk preferences when planning the optimal capacity of multiple distributed generators in the VPP. [ABSTRACT FROM AUTHOR]

Currently, the transmission system operators (TSOs) from Portugal and Spain do not procure a frequency containment reserve (FCR) through market mechanisms. In this context, a virtual power plant (VPP) that aggregates sources, such as wind and solar power and hydrogen electrolyzers (HEs), would benefit from future participation in this ancillary service market. The methodology proposed in this paper allows for quantifying the revenues of a VPP that aggregates wind and solar power and HEs, considering the opportunity costs of these units when reserving power for FCR participation. The results were produced using real data from past FCR market sessions. Using market data from 2022, a VPP that aggregates half of the HEs and is expected to be connected in the country by 2025 will have revenues over EUR 800k, of which EUR 90k will be HEs revenues. [ABSTRACT FROM AUTHOR]

In virtual power plants, diverse business scenarios involving user data, such as queries, transactions, and sharing, pose significant privacy risks. Traditional attribute-based encryption (ABE) methods, while supporting fine-grained access, fall short of fully protecting user privacy as they require attribute input, leading to potential data leaks. Addressing these limitations, our research introduces a novel privacy protection scheme using zero-knowledge proof and distributed attribute-based encryption (DABE). This method innovatively employs Merkel trees for aggregating user attributes and constructing commitments for zero-knowledge proof verification, ensuring that user attributes and access policies remain confidential. Our solution not only enhances privacy but also fortifies security against man-in-the-middle and replay attacks, offering attribute indistinguishability and tamper resistance. A comparative performance analysis demonstrates that our approach outperforms existing methods in efficiency, reducing time, cost, and space requirements. These advancements mark a significant step forward in ensuring robust user privacy and data security in virtual power plants. [ABSTRACT FROM AUTHOR]

As distributed energy resources (DER) are increasing, a variety of business models and markets are appearing. Business models such as virtual power plants (VPPs) contribute to enhanced stability of the power system while improving the profits of participating resources. However, the uncertainty of the behavior of DERs and the resulting stability issues are a major barrier to DER deployment and require a coordination framework between grid operators and business models to overcome. Therefore, in this paper, a hierarchical robust day-ahead coordination of a VPP and distribution system operator (DSO) based on the local market is proposed to improve the voltage stability of the distribution network by considering the uncertainty of DERs. For fairness reasons, the contribution of DERs to the distribution system can be addressed by a market-based approach. Local markets are divided into local energy markets (LEM) and local flexibility markets (LFM), which can consider the uncertainty and profitability of DERs. First, the DER determines the seller and buyer status of the LEM based on its self- scheduling with expected prices and is matched based on a double-auction mechanism. At this time, the worst-case scenario of load and renewable energy generation predicted by the Gaussian process (GP) is considered. The mismatched amounts in the LEM are aggregated by the VPP and bid into the wholesale energy market (WEM) operated by the TSO (Transmission system operator). Subsequently, DSO operates a LFM that coordinates microgrid (MG) and VPP on the distribution grid to increase voltage stability. The LFM and WEM problems are considered as interval optimization in the form of mixed integer linear programming and are applied together. The flexibility supply resulting from the adjustment of VPP is compensated by a loss-sensitivity-based flexibility price. Since voltage stability exists in upper and lower bounds, the upper and lower bound of net load through GP-based forecasting is considered along with the linearized network constraints. The results are validated through Monte-Carlo simulation-based scenarios on IEEE benchmark system in MATLAB environment. As a result, it is proved that the proposed model improves the distribution network voltage stability and the profit of the participants.

The virtual power plants are aggregations of distributed generators, grid-connected devices in user side. The operation of virtual power plants affects the economic benefits and environmental benefits. However, due to the stochastic and fluctuating nature of power generation from renewable energy sources, the optimal scheduling problem for the virtual power plant containing high-dimensional random variables is difficult to solve. The conic power flow constraints are considered in the virtual power plant day-ahead optimal scheduling models to maximize the revenue by virtue of optimizing the flexible resources such as energy storage system and interruptible load. To quantify the uncertainties in the optimization problem, this paper proposes a network state-based power scenario reduction strategy for renewable energy generation, where typical scenarios are selected by the state of the grid voltage. The proposed day-ahead scheduling model is a mixed-integer, nonlinear, large-scale, stochastic optimization problem with high dimensional random variables, which is difficult to be solved directly by traditional centralized method. By temporally decoupling the charging and discharging model of energy storage systems, a distributed optimization algorithm is proposed based on multi-temporal power flow decoupling optimization model. The simulation results show that the proposed distributed optimization algorithm has high accuracy and good convergence, the virtual power plant can achieve day-ahead optimal scheduling and effectively promote renewable energy accommodation under the security and reliability of the grid.

A virtual power plant (VPP) has the ability to aggregate numerous decentralized distributed energy resources using advanced control technology, offering a promising approach for low-carbon development. In order to enhance the VPP’s contribution to reducing carbon emissions, a bi-level framework is proposed that incorporates an integrated energy-carbon price response mechanism. This model allows VPPs to participate in a multi-energy system through a multi-agent Stackelberg game framework. Initially, a transaction model is established where the power distribution system operator and the gas distribution system operator act as leaders, while the virtual power plant operator acts as a follower in the multi-energy system. Subsequently, an integrated energy-carbon pricing method, rooted in carbon emission flow theory, is introduced to encourage VPPs to proactively adjust their energy-use and trading strategies within multi-energy systems, thereby promoting multi-principal interactive trading. To achieve a distributed solution among multiple entities while maintaining the privacy of each entity’s information, the adaptive step-size alternating direction multiplier method is employed. The feasibility and effectiveness of the proposed model and method are then demonstrated through case studies.

The Virtual Power Plant (VPP) is a promising solution to power systems' challenges. However, its energy management system (EMS) faces challenges due to centralized Big-Data analysis, i.e., complexity and high computational cost. Decentralized strategies, e.g., federated learning (FL), have been proposed to mitigate these challenges. Although conventional FL can be implemented in VPPs, its effectiveness is limited by its typical structure, which relies on single-pattern modeling. Indeed, this particular structure leads to delays and inaccuracies in optimizing the heterogeneous generator dispatch patterns, particularly renewables. Hence, addressing this critical hindrance is essential for FL's efficient and effective deployment in VPPs. To overcome this drawback, this paper proposes novel VPP modeling utilizing multi-task FL modernized by multi-pattern modeling to decentralize the EMS processing. The proposed FL defines electricity generators' agents as clients collaborating to train the decentralized local data coordinated by a central server. The innovative idea is to create a novel multi-pattern-multi-task FL for electricity generation prediction so that the generator's agents learn to provide an optimized dispatch. Thus, the conventional multi-task FL is improved by proposing an optimized neural-network clustering technique. Simulation results demonstrate that multi-pattern-multi-task FL is 39%-41% faster than the best-reported methods, i.e., teaching–learning-based and honey-bee-mating optimization algorithms.© 2017 Elsevier Inc. All rights reserved.