This paper proposes a novel model for the day-ahead self-scheduling problem of a virtual power plant trading in both energy and reserve electricity markets. The virtual power plant comprises a ...conventional power plant, an energy storage facility, a wind power unit, and a flexible demand. This multi-component system participates in energy and reserve electricity markets as a single entity in order to optimize the use of energy resources. As a salient feature, the proposed model considers the uncertainty associated with the virtual power plant being called upon by the system operator to deploy reserves. In addition, uncertainty in available wind power generation and requests for reserve deployment is modeled using confidence bounds and intervals, respectively, while uncertainty in market prices is modeled using scenarios. The resulting model is thus cast as a stochastic adaptive robust optimization problem, which is solved using a column-and-constraint generation algorithm. Results from a case study illustrate the effectiveness of the proposed approach.
Virtual power plants (VPPs) have emerged as a way to coordinate and control the growing number of distributed energy resources (DERs) within power systems. Typically, VPP models have focused on ...financial or commercial outcomes and have not considered the technical constraints of the distribution system. The objective of this article is the development of a technical VPP (TVPP) operational model to optimize the scheduling of a diverse set of DERs operating in a day-ahead energy market, considering grid management constraints. The effects on network congestion, voltage profiles, and power losses are presented and analyzed. In addition, the thermal comfort of the consumers is considered and the tradeoffs between comfort, cost, and technical constraints are presented. The model quantifies and allocates the benefits of the DER operation to the owners in a fair and efficient manner using the Vickrey-Clarke-Grove mechanism. This article develops a stochastic mixed-integer linear programming model and various case studies are thoroughly examined on the IEEE 119 bus test system. Results indicate that electric vehicles provide the largest marginal contribution to the TVPP, closely followed by solar photovoltaic (PV) units. Also, the results show that the operations of the TVPP improve financial metrics and increase consumer engagement while improving numerous technical operational metrics. The proposed TVPP model is shown to improve the ability of the system to incorporate DERs, including those from commercial buildings.
This paper presents a mathematical model for the energy bidding problem of a virtual power plant (VPP) that participates in the regular electricity market and the intraday demand response exchange ...(DRX) market. Different system uncertainties due to the intermittent renewable energy sources, retail customers' demand, and electricity prices are considered in the model. The DRX market enables a VPP to purchase demand response services, which can be treated as "virtual energy resources," from several demand response providers to reduce the penalty cost on the deviation between the day-head bidding and the real-time dispatch. This could increase the expected profit and the renewable energy utilization of the VPP. The overall energy bidding problem is modeled as a three-stage stochastic program, which can be solved efficiently by the scenario-based optimization approach. Extensive numerical results show that the DRX market participation can improve the VPP's energy management.
•Conducted a feasibility study of urban virtual power plant (VPP) with the goal of energy self-sufficiency.•Identified the energy-saving and economic potential of VPP for the demand side.•Applied ...Shapley value-based cooperative game theory, aiming to benefit both the plant and demand sides.•Provided policy and financial references for promoting the VPP development under similar structures for markets/regimes.
As most distributed energy resources (DERs) are accessible in urban areas, interest has increased in regards to evaluating the potential advantages from introducing virtual power plants (VPPs) comprised of DERs into such areas. A VPP provides a solution for improving an energy self-sufficiency rate, as an alternative to expanding the capacity of a conventional power plant (CPP). This research proposed a comprehensive method for analyzing the feasibility of using a VPP to benefit both the plant and demand sides. First, the energy-saving potential of a VPP composed of a photovoltaic (PV) and energy storage system (ESS) was explored, based on historical monitoring data in a Japanese smart community called Higashida District (with a size of approximately 1.2 km2). Second, the economic performance of the VPP was evaluated based on a payback period and total life cycle cost analysis. Then, considering the imbalance of the benefits between the demand and plant sides, cooperative game theory was applied to explore the cooperation potential. The influence of government subsidy policies on both the plant and demand sides was a simultaneous concern. Finally, the profit of the alliance, comprising both the demand and plant sides was allocated, based on the Shapley value. This study highlights the excellent energy-saving potential from implementing a VPP. The results show that there is substantial economic cooperation potential between the demand and plant sides. In addition, both the plant and demand sides have better profits in cooperative games than with non-cooperation. This research provides policy guidance for the Japanese government to promote VPPs in the future, and provides a solution for coordinating the profit allocations of the plant and demand sides.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZRSKP
Due to the increasing penetration of distributed energy resources (DERs), power system operators face significant challenges of ensuring the effective integration of DERs. The virtual power plant ...(VPP) enables DERs to provide their valuable services by aggregating them and participating in the wholesale market as a single entity. However, the available capacity of VPP depends on its DER outputs, which is time varying and not exactly known when the independent system operator runs the day-ahead unit commitment engine. In this study, we develop a model to evaluate the physical characteristics of the VPP, i.e., its maximum capacity and ramping capabilities, given the uncertainty in wind power output and load consumption. The proposed model is based on a distributionally robust optimization approach that utilizes moment information (e.g., mean and covariance) of the unknown parameter. We reformulate the model as a binary second-order conic program and develop a separation framework to address it. We first solve a two-stage problem and then benchmark it with a multi-stage case. Case studies are conducted to show the performance of the proposed approach.
In this paper, we propose a new framework for the optimal virtual power plant (VPP) energy management problem considering correlated demand response (CDR). Our objective is to minimize the VPP ...operating cost while maintaining the power quality of the system. We formulate a risk-constrained two-stage stochastic program to address uncertainties in day-ahead and real-time electricity prices, renewable energy source's generation processes, and the CDR relationship. The VPP can also maintain cooling and heating balances by coordinating combined cooling, heating, and power production and CDR units. Extensive simulation results show that the VPP can minimize the operating cost and ensure the energy balance and power quality by coordinating components in the framework we propose.
This paper proposes a novel approach for the offering strategy of a virtual power plant that participates in the day-ahead and the real-time energy markets. The virtual power plant comprises a ...conventional power plant, a wind-power unit, a storage facility, and flexible demands, which participate in the day-ahead and the real-time markets as a single entity in order to optimize their energy resources. We model the uncertainty in the wind-power production and in the market prices using confidence bounds and scenarios, respectively, which allows us to formul-ate the strategic offering problem as a stochastic adaptive robust optimization model. Results of a case study are provided to show the applicability of the proposed approach.
This article addresses the optimal bidding strategy problem of a virtual power plant (VPP) participating in the day-ahead (DA), real-time (RT) and spinning reserve (SR) markets (SRMs). The VPP ...comprises a number of dispatchable energy resources (DERs), renewable energy resources (RESs), energy storage systems (ESSs) and a number of customers with flexible demand. A two-stage risk-constrained stochastic problem is formulated for the VPP scheduling, where the uncertainty lies in the energy and reserve prices, RESs production, load consumption, as well as calls for reserve services. Based on this model, the VPP bidding/offering strategy in the DA market (DAM), RT market (RTM) and SRM is decided aiming to maximize the VPP profit considering both supply and demand-sides (DS) capability for providing reserve services. On the other hand, customers participate in demand response (DR) programs by using load curtailment (LC) and load shifting (LS) options as well as by providing reserve service to minimize their consumption costs. The proposed model is implemented on a test VPP and the optimal decisions are investigated in detail through a numerical study. Numerical simulations demonstrate the effectiveness of the proposed scheduling strategy and its operational advantages and the computational effectiveness.
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