This paper analyzes the technical and economic possibilities of integrating distributed energy resources (DERs) and energy-storage systems (ESSs) into a virtual power plant (VPP) and operating them as a single power plant. The purpose of the study is to assess the economic efficiency of the VPP model, which is influenced by several factors such as energy price and energy production. Ten scenarios for the VPP were prepared on the basis of the installed capacities of a hydropower plant (HPP), rooftop solar photovoltaic (PV), and energy-storage system (ESS), as well as weather conditions, in Poland. On the basis of technical conditions, it was assumed that the maximum power capacity of the ESS equaled 1.5 MW. The economic efficiency analysis presented in this paper demonstrated that, in seven years, the VPP will achieve a positive value of the net present value (NPV) for a scenario with 0.5 MW battery storage and rainy summers. Furthermore, sensitivity analysis was conducted on price factors and DER production volume. The price variable had a major impact on the NPV value for all scenarios. The scenario with a 0.5 MW battery and typical summers was highly sensitive to all factors, and its sensitivity decreased as the ESS capacity grew from 0.5 to 1.5 MW.

The article presents calculations and power flow of a real virtual power plant (VPP), containing a fragment of low and medium voltage distribution network. The VPP contains a hydropower plant (HPP), a photovoltaic system (PV) and energy storage system (ESS). The purpose of this article is to summarize the requirements for connection of generating units to the grid. Paper discusses the impact of the requirements on the maximum installed capacity of distributed energy resource (DER) systems and on the parameters of the energy storage unit. Firstly, a comprehensive review of VPP definitions, aims, as well as the characteristics of the investigated case study of the VPP project is presented. Then, requirements related to the regulation, protection and integration of DER and ESS with power systems are discussed. Finally, investigations related to influence of DER and ESS on power network condition are presented. One of the outcomes of the paper is the method of identifying the maximum power capacity of DER and ESS in accordance with technical network requirements. The applied method uses analytic calculations, as well as simulations using Matlab environment, combined with real measurement data. The obtained results allow the influence of the operating conditions of particular DER and ESS on power flow and voltage condition to be identified, the maximum power capacity of ESS intended for the planed VPP to be determined, as well as the influence of power control strategies implemented in a PV power plant on resources available for the planning and control of a VPP to be specified. Technical limitations of the DER and ESS are used as input conditions for the economic simulations presented in the accompanying paper, which is focused on investigations of economic efficiency.

The Power Grid Initiative is currently engaged in persistent endeavors to convert the conventional power grid into a smart grid with the objective of enhancing the operation of the power system. As per the United States Department of Energy, the contemporary power grid comprises a range of constituents such as information management systems, communication technologies, field devices, and other related components. The optimal functioning of said components yields enhanced power quality, thereby reducing power losses and augmenting the dependability of the power provision. During the operational process, the interdependence of interconnected components can present various challenges, including load forecasting, voltage stability evaluation, and power grid security. The present investigation involves the utilization of an artificial intelligence network (ANN) to facilitate the training and prediction of the nodal voltage level pertaining to the IEEE 4-bus system. Four discrete models have been postulated to authenticate the efficacy of the Artificial Neural Network (ANN) methodology. The findings indicate that the ensemble of models exhibits superior performance compared to other models in forecasting the nodal voltage stability of the aforementioned electrical system. The ensemble model attains the highest accuracy rate of 98.73%. Additionally, the mean squared error and mean absolute error exhibit the best (lowest) values of 0.0095 and 0.0141, respectively. The outcomes obtained indicate the efficacy of the Artificial Neural Network (ANN) methodology, particularly in the ensemble configuration, for predicting the voltage stability of the electrical power grid. INDEX TERMSVoltage stability, machine learning, artificial neural networks, forecasting electric power system, ensemble learning. I. INTRODUCTIONThe conventional electric power grid is being converted into a network that is controlled electronically as part of the Energy Grid Initiative. The traditional grid was previously controlled by an electromechanically operated system. According to the Power Grid System Report, [1] published by the US Department of Energy, the components that make up Power Grid systems include information management, control technology, digital sensing, communication technologies, and field devices. These components work in conjunction with each other to control a number of distinct electrical processes. These technologies collaboratively participate in aThe associate editor coordinating the review of this manuscript and approving it for publication was Youngjin Kim .