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.

Many modern industries are equipped with on-site renewable generation and are normally connected to the grid. A battery energy storage system (BESS) can complement the intermittency of the available on-site renewable generation. The combination of the BESS and the renewable generation can operate as a microgrid. If the microgrid is properly sized and managed, it is possible to reduce the electricity-bill to have a huge saving in the electricity-cost. This paper proposes an energy management system for such industrial microgrids. The decisions to charge and discharge the BESS in the proposed energy management are usually constrained by the size of the energy storage. The proposed energy management strategy aims to optimize the operation of the industrial microgrid subject to the scalability of the BESS under uncertainties. The proposed optimization involves two-stages. In the first-stage of optimization, it determines the optimum size of the energy storage taking into account the cost of the BESS, and in the second-stage, it minimizes the cost of the microgrid operation based on the decision made in the first-stage. This proposed two-stage energy management strategy is formulated as a single-stage linear program that incorporates stochastic scenarios for addressing uncertainties. In addition, the proposed strategy also considers the various operating limits of the energy storage such as the efficiency, the charging and the discharging rates and considers the fading effect of the batteries of the BESS. The proposed strategy is then validated using two typical data-sets from two different industrial units in New South Wales, Australia.

This paper proposes a cooperative energy transaction model for a virtual power plant (VPP) developed as a bi-level optimization program based on the Stackelberg game in the deregulated electricity markets. The VPP integrates multiple renewable energy hubs consisting of electric vehicle charging stations with photovoltaic and battery energy storage systems. The proposed model derives co-optimized strategic decisions for the VPP operator in the day-ahead (joint active power, reserve, and reactive power) markets while considering the balancing market operation. The proposed model is reformulated as a mathematical program with equilibrium constraints (MPEC) using the Karush-Kuhn-Tucker conditions and the strong duality theorem. For an efficient solution, the MPEC is approximated as a mixed-integer linear program by applying the Fortuny-Amat transform on the complementarity and slackness conditions and the binary expansion on the bilinear terms. The uncertainties of the renewables, loads, and prices are modeled using stochastic scenarios. Finally, comparative case studies of a VPP in an IEEE 33 node active distribution network show that the proposed model improves the profit by strategic cooperative energy transactions.

Increasing environmental concerns have led to a push for renewable energy sources to be extensively used to reduce emissions. In this paper, we investigate a hybrid dynamic economic emission dispatch (HDEED) problem involving thermal, wind, and photovoltaic (PV) generation systems. Our formulation considers the stochastic nature of both wind and PV generated power, and differences because of mismatches between the actual and allocated wind and PV power. A hybrid backtracking search algorithm with sequential quadratic programming was used to minimize the total operational costs and emissions, while dispatching power to the committed generation units subject to all operational constraints. To verify the efficacy, we applied the proposed technique to solve the dynamic economic emission dispatch problem on five and ten unit test systems. The proposed technique was also used to solve the HDEED problem on IEEE 30 bus, 6-unit and IEEE 57 bus, 7-unit test systems, with and without renewable generation. The results of our numerical simulations show the efficiency of the proposed technique with respect to reducing operational costs and emissions. Moreover, our results show that by incorporating renewable energy into existing power systems, we can reduce operational costs and emissions.

Modern active distribution networks (ADNs) have become more resilient to service interruptions due to the flexibility offered by integrated distributed energy resources (DERs). Compared to network-owned DERs, directly controlled by the distribution system operator (DSO), a vast majority of DERs controlled by independent virtual power plants (VPPs) are much more difficult to coordinate. The coordination with the VPPs and the management of uncertainty are the two major obstacles in the utilization of these DERs in the ADN restoration (ADNR). This article proposes a hierarchical framework for the sequential ADNR to overcome these two obstacles. The proposed framework adaptively integrates the sequential ADNR planning and the VPP scheduling models based on a comprehensive DSO and VPP coordination scheme to quantify and include the flexibility of VPPs in the ADNR. Furthermore, to address the uncertainty of renewable DERs and loads, a chance-constraint (CC) approach is used in these models. As the probability distributions of the uncertain parameters are not fully known, a distributionally robust reformulation for CCs is proposed that ensures that the solution is robust to any uncertainty distribution defined within a moment-based ambiguity set. The DR reformulation is further linearized to achieve DRCC mixed-integer linear-programming models for ADNR planning and VPP scheduling. Finally, its effectiveness is evaluated on the IEEE 123 node ADN.