•Design a novel structure of virtual power plant connected with gas-power plant carbon capture, power-to-gas and waste incineration power (GPW-VPP).•Propose a nearly-zero carbon optimal operation ...model for GPW-VPP based on the information gap decision theory and fuzzy satisfaction theory.•Construct a Nash negotiation-based benefit allocation strategy for GPW-VPP considering the contribution factors of risks, benefits and carbon emissions.•Select the Lankao Rural Energy Revolution Pilot as the simulation system for verifying the effectiveness and applicability of the proposed.•Discuss the optimal operation strategy of the GPW-VPP under the extremely worst scenario.
Aiming at utilizing a large number of distributed energy sources in rural areas such as straw and garbage biomass, rooftop photovoltaics, and decentralized wind power, this study designed a novel structure of a virtual power plant connected with gas-power plant carbon capture (GPPCC), power-to-gas (P2G), and waste incineration power (WI), namely, a GPW-VPP. Then, the information gap decision theory (IGDT) and fuzzy satisfaction method were applied to construct a nearly-zero carbon optimal operation model. In this model, the maximum revenue and minimum carbon emissions were selected as the initial objectives, which were converted into one maximum satisfaction objective. Three uncertainty variables, namely, wind power, photovoltaic power, and user’s load, were described using the IGDT. Secondly, to optimize the distribution of the cooperative operation revenue for the entities in GPW-VPP, a Nash negotiation-based benefit allocation strategy is established considering the multidimensional contribution factors of risks, benefits, and carbon emissions. Finally, the Lankao Rural Energy Revolution Pilot program in China was selected as the case study, the results showed: (1) GPW-VPP can aggregate and utilize different types of distributed energy sources such as rural wind power plants (WPPs) and photovoltaic power generation (PVs) to realize the electricity–carbon–electricity cycle effect. (2) The proposed operation optimization model can measure the uncertainty risk and formulate an optimal plan considering the above dual objectives. When the deviation coefficient of the predicted objectives is 0.5, the uncertainty degree is 0.142, and the cost of the decision plan is less than the expected cost of decision maker. Compared with the maximum revenue objective, the operation revenue and carbon emissions reduced by 4.6% and 35.76% under the comprehensive optimization objective, respectively, (3) The proposed benefit distribution strategy can be used to formulate a better benefit distribution plan that meets the comprehensive contributions of multiple entities. Affected by the risk of output uncertainty, the benefit proportion of WPP and PV increased, but it was 1.64% lower than that in the traditional distribution plan. Affected by carbon emissions, the benefit proportion of biomass power generation decreased, but it was 0.57% higher than that in the traditional distribution plan. Overall, the proposed operation optimization model and benefit distribution strategy can balance the interest requirements of different entities and promote the optimal aggregation and utilization of rural distributed energy resources, which is conducive to the realization of a clean and low-carbon transformation of the overall energy structure.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Due to different viewpoints, procedures, limitations, and objectives, the scheduling problem of distributed energy resources (DERs) is a very important issue in power systems. This problem can be ...solved by considering different frameworks. Microgrids and Virtual Power Plants (VPPs) are two famous and suitable concepts by which this problem is solved within their frameworks. Each of these two solutions has its own special significance and may be employed for different purposes. Therefore, it is necessary to assess and review papers and literature in this field. In this paper, the scheduling problem of DERs is studied from various aspects such as modeling techniques, solving methods, reliability, emission, uncertainty, stability, demand response (DR), and multi-objective standpoint in the microgrid and VPP frameworks. This review enables researchers with different points of view to look for possible applications in the area of microgrid and VPP scheduling.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Virtual power plants (VPPs) have become a driving force for the decentralized energy industry, due to their efficient management and control of distributed energy resources. Most of the operation ...strategies for VPPs are designed based on the day-ahead forecasts. However, the prediction errors of the renewable energy sources (RES) and loads in the power dispatch schedule can lead to a suboptimal operation. In this article, an adaptive and predictive energy management strategy for a real-time optimal operation of VPPs is proposed based on the model predictive control technique with a feedback correction (FC) to compensate for the prediction error. This strategy has two parts: 1) receding-horizon optimization (RHO), and 2) FC. In the first part, a hybrid prediction algorithm based on the integration of the time-series model and the Kalman filter is used to forecast the output powers of RES and the loads. Based on the prediction, the RHO model schedules the operation following the latest forecast information. In the second part, the receding schedule is adjusted based on the fast-rolling gray model's ultrashort-term error prediction. The FC is applied to minimize the adjustments for compensating the prediction error. The proposed strategy is implemented on a VPP in a real electricity distribution system in New South Wales, Australia. The simulation results demonstrate the effectiveness of the proposed strategy with a better tracking of the actual available resources and a minimal mismatch between demand and supply.
This paper presents a two-layer energy management model (EMM) in the smart distribution network (SDN) considering flexi-renewable virtual power plants (FRVPPs) that participate in the day-ahead ...energy and reserve markets. The first layer of EMM is applied to the FRVPPs to maximize their profit in the proposed markets subjected to the constraints of renewable and flexible sources with considering coordination between these sources and VPP operator (VPPO). Also, the second layer of EMM creates coordination between VPPOs and the distribution system operator to manage the SDN based on minimizing the summation of network energy loss and voltage deviation function as a linear normalized objective function while it subjects to the linear format of AC optimal power flow equations. This model contains uncertainties of load, market price, maximum power of renewable energy sources and demand of flexible sources, where stochastic programming is used to model these uncertain parameters. The proposed model includes bi-level optimization model that is solved by the Benders decomposition approach to achieve an optimal solution at low calculation time. Finally, the capabilities of the proposed model have been investigated by implementing on the IEEE 69-bus distribution network.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Current power networks and consumers are undergoing a fundamental shift in the way traditional energy systems were designed and managed. The bidirectional peer-to-peer (P–P) energy transactions ...pushed passive consumers to be prosumers. The future smart grid or the internet of energy (IoE) will facilitate the coordination of all types of prosumers to form virtual power plants (VPP). The paper aims to contribute to this growing area of research by accumulating and summarizing the significant ideas of the integration of distributed prosumers and small-scale VPP to the internet of energy (IoE). The study also reports the characteristics of IoE in comparison to the traditional grid and offers some valuable insights into the control, management and optimization strategies of prosumers, distributed energy resources (DERs) and VPP. As bidirectional P–P energy transaction by the prosumers is a crucial element of IoE, their management strategies including various demand-response approach at the customers’-levels are systematically summarized. The integration of DERs and prosumers to the VPP considering their functions, infrastructure, type, control objectives are also reviewed and summarized. Various optimization techniques and algorithm, and their objectives functions and the types of mathematical formulation that are used to manage the DERs and VPP are discussed and categorized systematically. Finally, the factors which affect the integration of DERs and prosumers to the VPP are identified.
•It provides insights into the control, management and optimization strategies of prosumers, DER and VPP.•The integration of DER and prosumers to the VPP considering their functions, type and control objectives are summarized.•Various optimization techniques and algorithm, and their objectives functions to manage the DER and VPP are reported.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
MES (multi-energy systems) whereby electricity, heat, cooling, fuels, transport, and so on optimally interact with each other at various levels (for instance, within a district, city or region) ...represent an important opportunity to increase technical, economic and environmental performance relative to "classical" energy systems whose sectors are treated "separately" or "independently". This performance improvement can take place at both the operational and the planning stage. While such systems and in particular systems with distributed generation of multiple energy vectors (DMG (distributed multi-generation)) can be a key option to decarbonize the energy sector, the approaches needed to model and relevant tools to analyze them are often of great complexity. Likewise, it is not straightforward to identify performance metrics that are capable to properly capture costs and benefits that are relating to various types of MES according to different criteria. The aim of this invited paper is thus to provide the reader with a comprehensive and critical overview of the latest models and assessment techniques that are currently available to analyze MES and in particular DMG systems, including for instance concepts such as energy hubs, microgrids, and VPPs (virtual power plants), as well as various approaches and criteria for energy, environmental, and techno-economic assessment.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
To address the model inaccuracy and uncertainty of virtual power plants (VPPs), a model-free economic dispatch approach for multiple VPPs is studied in this article, which does not rely on an ...accurate environmental model. An adversarial safe reinforcement learning approach is proposed, which promotes the safety of the actions and makes the model robust to deviations between the training and testing environments. Moreover, a two-stage reinforcement learning framework is formulated based on the proposed algorithm. The dispatch policy is pretrained in the simulator and then fine-tuned in the real-world environment. The numerical simulations illustrate that the proposed approach is adaptive to the deviation between the training and testing environments, and it provides higher robustness to the noise of the network parameters and uncertainty of the VPPs' power outputs. The scalability and superiority of the proposed approach are verified by comparing it with existing methods.
•A deep-learning-based approach constructed on B-LSTM is employed to handle the uncertainties.•An optimal bidding strategy of a VPP for participating in the electricity markets is presented.•The ...proposed approach results in precise outcomes, with only a 4.1% error in comparison with real data.•A comprehensive study is presented for implementing EVs and RERs in the VPP structure.
Various challenges and opportunities are recognized by increasing the penetration of distributed energy resources (DERs) in power systems. In this regard, the concept of virtual power plants (VPPs) has been proposed to tackle the imposed challenges and exploit the offered opportunities. In this paper, an optimal bidding strategy of a VPP participating in the day-ahead frequency regulation market (FRM) and the energy market (EM) is proposed. A comprehensive form of a VPP that contains various DERs has a high potential in FRM due to its fast response. In this study, to strengthen the VPP performance in FRM, which has strict rules with steep penalties, a deep learning-based approach known as bi-directional long short-term memory (B-LSTM) network is employed. Forming a precise estimation on the VPP internal components (i.e. renewable energy resources (RESs) generation, VPP load demand, and electric vehicles (EVs) behavior) and market signals (i.e. different electricity prices, regulation signals) is a crucial factor in the VPP optimal bidding strategy in the day-ahead markets. This accurate forecasting obtained by B-LSTM helps VPP operators to fulfill the day-ahead awarded bids and avoid substantial penalties in the real-time market. The CAISO market rules are considered to form the test environment. The numerical results illustrate the success of the proposed method in handling the uncertainty of the various parameters by providing accurate results (3.75% error in comparison with real data); which, will increase the VPP’s profit significantly. Furthermore, the diversification of the VPP resources through implementation of distributed generations (DGs), energy storages (ESs), and EVs and their mobilization in FRM yields 470.76 $, 550 $, and 33.58 $ profit, respectively—which constitutes 24.41% of the VPP's total profit for real data.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In the transition to a two-sided electricity market, energy users are turning into prosumers who own the flexible distributed energy resources (DERs) and have the potential to provide services to the ...power system. Virtual power plants (VPPs) aggregate DERs to join the electricity market and respond to system signals. It is urgent to develop a new pricing mechanism for VPPs to allocate the payoff from the electricity market to prosumers. This paper proposes a customized rebate package pricing mechanism for a VPP retailer to reward prosumers for supporting the power system. The retailer's pricing strategies are determined based on a Stackelberg game, considering the heterogeneous prosumers' dynamic selecting process based on an evolutionary game. The extended replicator dynamics is proposed to take the future payoff into account and guarantee the evolutionary equilibrium. Moreover, a new reinforcement learning algorithm based on the Cross learning model is developed to solve the evolutionary game with less computational effort. The simulation results verify the effectiveness of the proposed customized rebate package pricing mechanism, which can efficiently reward prosumers' flexible resources in supporting the system while maximizing the retailer's utility to achieve a win-win outcome.
Battery energy storage (BES) and demand response (DR) are two important resources to increase the operational flexibility of a virtual power plant (VPP) and thus reduce the economic risks that VPP ...faces in the short-term electricity market. This article develops a data-driven approach for VPP resource planning (VRP), in which BES sizing and DR customer selection are optimized synergistically to maximize VPP's profit in the electricity market. Heterogeneity in DR potential across individual customers is considered in the planning framework by utilizing the knowledge learnt from smart meter data. The overall VRP problem is formulated by a risk-managed, multistage stochastic programming framework to address the uncertainties from the intermittent renewable energy sources, load demands, market prices, and DR resources. Case studies compare the VRP results under two market imbalance settlement settings, namely, penalty-charged and penalty-free markets. The results demonstrate that jointly optimizing BES and DR customer selection leveraging the smart meter data can improve the VPP's expected profit under both market settings.
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