Source-storage interaction optimization and scheduling based on centralized control virtual power plant

This article describes problems related to the operation of a virtual micro power plant at the Faculty of Electrical Engineering (FEE), Czestochowa University of Technology (CUT). In the era of dynamic development of renewable energy sources, it is necessary to create alternative electricity management systems for existing power systems, including power transmission and distribution systems. Virtual power plants (VPPs) are such an alternative. So far, there has been no unified standard for a VPP operation. The article presents components that make up the VPP at the FEE and describes their physical and logical structure. The presented solution is a combination of several units operating in the internal power grid of the FEE, i.e., wind turbines, energy storage (ES), photovoltaic panels (PV) and car charging stations. Their operation is coordinated by a common control system. One of the research goals described in the article is to optimize the operation of these components to minimize consumption of the electric energy from the external supply network. An analysis of data from the VPP management system was carried out to create mathematical models for prediction of the consumed power and the power produced by the PVs. These models allowed us to achieve the assumed objective. The article also presents the VPP data processing results in terms of detecting outliers and missing values. In addition to the issues discussed above, the authors also proposed to apply the Prophet model for short-term forecasting of the PV farm electricity production. It is a statistical model that has so far been used for social and business research. The authors implemented it effectively for technical analysis purposes. It was shown that the results of the PV energy production forecasting using the Prophet model are acceptable despite occurrences of missing data in the investigated time series.

A Virtual Power Plant (VPP) is a cloud-based platform implement to collect and control decentralized power resources like photo voltaic arrays, thermal units, wind turbines, battery storage units (BSU), as well as electric vehicles (EV). VPP task is to delivers grid services and capitalize on the value of these distributed mentioned sources to costumers and grid operators. The idea of these systems established in the early 2000s, matching with the raising integration of renewable energy resources as extra inputs to the power grid and the increasing demand for fast, flexible responses inside power systems. Early VPP tested were achieved in Germany, with the objective of assessing their technical and economic feasibility, beside their potential gifts to grid reliability and stability. Ended the last years, VPPs have extended significantly, pushed by development in digital technologies. This work introduces a complete review of recent advances in Virtual Power Plant (VPP) technologies, as well as their structural design, control strategies, and overall feasibility in modern energy systems. It explores how VPPs integrate diverse distributed energy resources—such as solar photovoltaics, wind turbines, energy storage units, and demand response into a unified, cloud-based operational platform capable of delivering reliable grid services.

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.

To make full use of distributed energy resources to meet load demand, this study aggregated wind power plants (WPPs), photovoltaic power generation (PV), small hydropower stations (SHSs), energy storage systems (ESSs), conventional gas turbines (CGTs) and incentive-based demand responses (IBDRs) into a virtual power plant (VPP) with price-based demand response (PBDR). Firstly, a basic scheduling model for the VPP was proposed in this study with the objective of the maximum operation revenue. Secondly, a risk aversion model for the VPP was constructed based on the conditional value at risk (CVaR) method and robust optimization theory considering the operating risk from WPP and PV. Thirdly, a solution methodology was constructed and three cases were considered for comparative analyses. Finally, an independent micro-grid on an industrial park in East China was utilized for an example analysis. The results show the following: (1) the proposed risk aversion scheduling model could cope with the uncertainty risk via a reasonable confidence degree β and robust coefficient Γ. When Γ ≤ 0.85 or Γ ≥ 0.95, a small uncertainty brought great risk, indicating that the risk attitude of the decision maker will affect the scheduling scheme of the VPP, and the decision maker belongs to the risk extreme aversion type. When Γ ∈ (0.85, 0.95), the decision-making scheme was in a stable state, the growth of β lead to the increase of CVaR, but the magnitude was not large. When the prediction error e was higher, the value of CVaR increased more when Γ increased by the same magnitude, which indicates that a lower prediction accuracy will amplify the uncertainty risk. (2) when the capacity ratio of (WPP, PV): ESS was higher than 1.5:1 and the peak-to-valley price gap was higher than 3:1, the values of revenue, VaR, and CVaR changed slower, indicating that both ESS and PBDR can improve the operating revenue, but the capacity scale of ESS and the peak-valley price gap need to be set properly, considering both economic benefits and operating risks. Therefore, the proposed risk aversion model could maximize the utilization of clean energy to obtain higher economic benefits while rationally controlling risks and provide reliable decision support for developing optimal operation plans for the VPP.

11 - Risk-averse scheduling of virtual power plants considering electric vehicles and demand response

The implementation of demand response (DR) could contribute to significant economic benefits meanwhile simultaneously enhancing the security of the concerned power system. A well-designed carbon emission trading mechanism provides an efficient way to achieve emission reduction targets. Given this background, a virtual power plant (VPP) including demand response resources, gas turbines, wind power and photovoltaics with participation in carbon emission trading is examined in this work, and an optimal dispatching model of the VPP presented. First, the carbon emission trading mechanism is briefly described, and the framework of optimal dispatching in the VPP discussed. Then, probabilistic models are utilized to address the uncertainties in the predicted generation outputs of wind power and photovoltaics. Demand side management (DSM) is next implemented by modeling flexible loads such as the chilled water thermal storage air conditioning systems (CSACSs) and electric vehicles (EVs). On this basis, a mixed integer linear programming (MILP) model for the optimal dispatching problem in the VPP is established, with an objective of maximizing the total profit of the VPP considering the costs of power generation and carbon emission trading as well as charging/discharging of EVs. Finally, the developed dispatching model is solved by the commercial CPLEX solver based on the YALMIP/MATLAB (version 8.4) toolbox, and sample examples are served for demonstrating the essential features of the proposed method.

This paper presents an efficient algorithm based on imperialist competitive algorithm for optimal energy management of a Virtual Power Plant (VPP) including different distributed generation units and energy storage devices. A collection of Distributed Energy Resources, energy storage devices, and controllable loads which are aggregated and then are managed by an Energy Management System and can operate as a single power plant is called VPP. The proposed approach employs imperialist competitive algorithm to minimize the total operating cost of the VPP, considering energy loss cost in a 24-hour time interval through 3 different scenarios while satisfying various operating constraints. This method has the benefit of escaping from the local optima while converging fast. In order to see the effectiveness and satisfying performance of the proposed algorithm, a case study including Renewable Energy Resources (RESs), storage battery, and controllable loads is studied as test system.

For the uncertainty of wind power, PV and load in virtual power plant (VPP), a two-stage optimal dispatching model considering the participation of demand-side response resources in VPP is developed from two stages: day-ahead and real-time, taking into account the forecast error and customer-side demand response resources. In the day-ahead stage, a day-ahead VPP optimal economic dispatch model is developed using price-based demand response and incentive-based demand response to optimize customer load curves with VPP operating cost as the target. In the real time stage, taking into account the regulation characteristics of the excitation type demand response, the real time power deviation is smoothed together with the internal generator set of VPP to realize the correction of the power output of the equipment before the day. Both phases of the model are modeled on MATLAB software and solved by the CPLEX optimization solver. The simulation results show that the introduction of price-based demand response and incentive-based demand response together can optimize the load profile of users better and reduce the operating cost of VPP; the two-stage optimal scheduling can smooth out the random fluctuations on both sides of the source load in the real-time phase and realize the economic and stable operation of VPP.

Due to the existence of wind turbines in virtual power plants (VPPs), to eliminate the fluctuation characteristics of the wind turbine in the VPP and quantify the impact of the capacity of the energy storage system (ESS) on VPPs’ profitability, a multi-objective optimal schedule model, including the operating cost of the VPP, operation cost of the ESS and the electricity cost between the VPP and the grid, was proposed, and was solved by a quantum-inspired evolutionary algorithm; the output of conventional distribution generation and the state of charge were analysed on two different load days; the impact of different sub-object weights on the ESS capacity and the ESS cost and VPP profit under different rated capacities were also investigated. The results showed that the wind turbines' output error can be eliminated by the ESS on peak load day and valley load day; different weights of the VPP operating cost and the trading power cost would result in different capacities of the ESS; and the VPP's profit would rise with the increase of the ESS capacity when the capacity of the ESS was less than 8 MW.

Integrated techno-economic modeling, flexibility analysis, and business case assessment of an urban virtual power plant with multi-market co-optimization

Virtual power plant (VPP) amalgamates diverse distributed resources, thereby unlocking the full potential of distributed energy’s dispatch capabilities. Energy storage is an effective means to address the uncertainty of renewable energy and achieve energy complementarity. This study meticulously evaluates the finite nature of traditional energy within VPP, the unpredictability associated with renewable energy outputs, and the constraints faced by energy storage devices. It introduces strategic operational frameworks for VPP through cooperation between VPP operator and energy storage provider managing new type of electric energy storage systems. With the dual objectives of amplifying the economic gains for VPP operator and maximizing benefits for energy storage provider, this research formulates a VPP economic low-carbon dispatch model, guided by a carbon tax framework. The improved particle swarm optimization algorithm is used to solve the proposed dispatch model. The numerical results confirmed that the collaboration between the VPP operator and the energy storage provider mitigates the issues caused by the variability of renewable energy outputs on the VPP, resulting in a mutually beneficial scenario for both the operator and the storage provider. Additionally, carbon emissions have been drastically decreased in response to low carbon targets.

Coordination of wind power producers with an energy storage system for the optimal participation in wholesale electricity markets

This paper provides a brief overview of the importance and how virtual power plants (VPPs) could participate in the Romanian electricity market in the future, as well as renewable energy integration into the Romanian Power System (RPS). Over the last decade, Romania has experienced a significant growth in its distributed energy resources (DERs) installed capacity base. VPPs concept aggregates diverse DERs, including solar photovoltaic (PV) arrays, wind turbines (WTs), battery energy storage systems (BESSs), and demand response units to operate as one big power plant which can trade on the same electricity market that today's suppliers do. VPPs integrate renewable energy and digitization based on Information and Communications Technologies (ICTs) and Artificial Intelligence (AI)-based predictive models to create a new energy platform for prosumers. VPPs solutions will process the energy production forecast and electricity price, then optimize the electricity sales plan, and if there is charge and discharges gathered, to obtain total profit.

To mitigate the risks posed by uncertainties in renewable energy output and Electric Vehicle (EV) travel patterns on the scheduling of Virtual Power Plants (VPPs), this paper proposes an optimal scheduling model for a VPP incorporating EVs based on Information Gap Decision Theory (IGDT). First, a Monte Carlo load forecasting model is established based on the behavioral characteristics of EV users, and a Sigmoid function is introduced to quantify the dynamic relationship between user response willingness and VPP incentive prices. Second, within the VPP framework, an economic optimal scheduling model considering multi-source collaboration is developed by integrating wind power, photovoltaics, gas turbines, energy storage systems, and EV clusters with Vehicle-to-Grid (V2G) capabilities. Subsequently, to address the uncertain parameters within the model, IGDT is employed to construct a bi-level decision-making mechanism that encompasses both risk-averse and opportunity-seeking strategies. Finally, a case study on a VPP is conducted to verify the correctness and effectiveness of the proposed model and algorithm. The results demonstrate that the proposed method can effectively achieve a 7.94% reduction in the VPP’s comprehensive dispatch cost under typical scenarios, exhibiting superiority in terms of both economy and stability.

A growing number of Distributed Energy Resources will be connected to the grid. These distributed energy resources have different technical capabilities, operational constraints and are distributed throughout the electricity network which results in an increasing operational complexity. Aggregators controlling these resources in a virtual power plant might need to reconfigure it in case of failing distributed energy resources (e.g. when the weather changes) or due to changing grid conditions on a system operators request. This means that both, aggregator and system operator, must maintain operational reserves such as frequency control which means also rising costs for the actors. Different approaches such as dynamic virtual power plants have been introduced to address these issues. Extending these approaches, the increasing automation and information as well as communication capabilities will allow the actors to exchange and share real-time data about these resource capabilities on demand, allowing the aggregator to dynamically reconfigure the virtual power plant with external capabilities of distributed energy resources prior operation and during runtime, allowing the virtual power plant to be adaptive towards changing situations. For this purpose a common, active registry system for energy resource capabilities is necessary. In this paper, we will describe a process to dynamically aggregate distributed energy resource capabilities a priori as well as during runtime allowing the adaptive reconfiguration of virtual power plants.