The penetration of renewable energies in the energy market has increased significantly over the last two decades due to environmental concerns and clean energy requirements. The principal advantage of renewable energy resources (RESs) over non‐RESs is that it has no direct carbonisation impact on the environment and that it has none of the global warming effects which are caused by carbon emissions. Furthermore, the liberalisation of the energy market has led to the realisation of the virtual power plant (VPP) concept. A VPP is a unified platform for distributed energy resources that integrates the capacities of various renewable energies together for the purpose of improving power generation and management as well as catering for the buying and selling of energy in wholesale energy markets. This review study presents a comprehensive review of existing approaches to planning, operation and scheduling of the VPP system. The methodologies that were adopted, their advantages and disadvantages are assessed in detail in order to benefit new entrants in the power system and provide them with comprehensive knowledge, techniques and understanding of the VPP concept.
Renewable energy sources are becoming more prevalent as a source of clean energy, and their integration into the power industry is speeding up. The fundamental reason for this is the growing global concern about climate change. However, the weather-dependent and uncertain nature of renewable generation raise questions about grid security particularly, when photovoltaics (PVs) and wind turbines (WTs) technologies are used. The incorporation of energy storage systems (ESS) in a virtual power plant (VPP) environment could compensate for the renewable generation's uncertainty. Where a VPP is a new concept that combines dispatchable and non-dispatchable energy sources, electrical loads, and energy storage units, enabling individual energy producers to participate in the electricity market. In this study, a market-based-VPP’s operational planning and design model is presented to assess the optimal active power dispatch of (WT, PV, and ESS) operating in the day-ahead electricity market to maximize social welfare (SW) considering the uncertainties associated with wind speed, solar irritation, and load demand. The Scenario-tree technique is applied to model the uncertainties of renewable energy sources and load demand. The proposed model performance is validated by simulation studies on a 16-bus UK generic distribution system (UKGDS). According to the simulation results, renewable energy sources and energy storage systems dispatched optimally active power to satisfy the load demand in the most efficient way possible.
Integrating decentralised energy sources into the traditional distribution networks can result in technical issues impacting the power quality. Innovative ideas are, therefore, needed to promote the transformation of systems to a smart grid. Distribution System operator (DSO) could make use of the flexibility of emerging technologies as a method to address these power quality issues. This study aims to present an overview of a local flexibility market (LFM) which will allow DSO requirements to be fulfilled through the (VPP) as an energy flexibility provider. The required optimization loads, generators and as well as storage units, are undertaken in the general algebraic modeling simulation (GAMS) environment. The aim of the optimization problem is to provide DSOs the opportunity to increase or curtail the local generations and loads in order to satisfy their requirement. The VPP will then be responsible for handling the relevant requests in real time to ensure the correct operating schedule of a resource is applied. The preliminary results of simulation studies presented in this paper have shown that the local market framework for flexibility could have potential for deferring investments in distribution network capacity, minimizing energy costs and improving the hosting capacity of distribution networks.
. In this study, a robust optimisation method (ROM) is proposed with aim to achieve optimal scheduling of virtual power plants (VPPs) in the day-ahead electricity markets where electricity prices are highly uncertain. Our VPP is a collection of various distributed energy resources (DERs), flexible loads, and energy storage systems that are coordinated and operated as a single entity. In this study, an offer and bid-based energy trading mechanism is proposed where participating members in the VPP setting can sell or buy to/from the day-ahead electricity market to maximise social welfare (SW). SW is defined as the maximisation of end-users benefits and minimisation of energy costs. The optimisation problem is solved as a mixed-integer linear programming model taking the informed decisions at various levels of uncertainty of the market prices. The benefits of the proposed approach are consistency in solution accuracy and traceability due to less computational burden and this would be beneficial for the VPP operators. The robustness of the proposed mathematical model and method is confirmed in a case study approach using a distribution system with 18-buses. Simulation results illustrate that in the highest robustness scenario, profit is reduced marginally, however, the VPP showed robustness towards the day-ahead market (DAM) price uncertainty
The penetration of renewable energy sources (RESs) in the electrical power system has increased significantly over the past years due to increasing global concern about climate change. However, integrating RESs into the power market is highly problematic. The output of RESs such as wind turbines (WTs) and photovoltaics (PVs) is highly uncertain. Their correlation with load demand is not always guaranteed, which compromises system reliability. Distributed energy resources (DERs), especially demand response (DR) programs and energy storage systems (ESSs), are possible options to overcome these operational challenges under the virtual power plant (VPP) setting. This study investigates the impact of using a DR program and battery energy storage system (BESS) on the VPP’s internal electricity market, and also cost-minimization analysis from a utility viewpoint. Three different constrained optimal power flow (OPF) problems are solved such as base case, DR case, and BESS case to determine total incurred costs, locational marginal prices (LMPs), and generator commitments. A scenario tree approach is used to model the uncertainties associated with WTs, PVs, and load demand. The proposed model is investigated on a 14-bus distribution system. The simulation results obtained demonstrate a favorable impact of DR and a BESS on renewable operational challenges.
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