This paper presents an optimal bidding strategy for a technical and commercial virtual power plant (VPP) in medium-term time horizon. A VPP includes various distributed energy resources (DERs) that can participate in the Pool and Futures markets. Although medium/long-term scheduling provides the opportunity to participate in the futures market, it also raises the possibility of unit failure. In this regard, the impact of distributed generation (DG) units’ failure, as an important challenge in VPP, is incorporated in the proposed model. The model is formulated as a risk-constrained two-stage stochastic problem. The VPP signs futures market contracts in the first stage, and in the second stage, it participates in the day-ahead (DA) market and manages its DERs. Long short-term memory neural network and scenario generation and reduction methods are used to capture the uncertainty parameters of electrical load, DA market prices, wind speed, and solar radiation in the proposed model. The performance of proposed model is investigated in different cases. The obtained results show that the VPP can compensate the losses caused by the DG units’ failure through taking advantage of the arbitrage opportunity.

This article presents an electric vehicle (EV)-integrated security constrained unit commitment (EV-SCUC) based on N−1 criteria for single line outage contingency under intermittency of load and renewable energy sources (RESs). Two EV control mechanisms are proposed. In first, EV balance control, financial targets are prioritized while flexibility is provided for the power network. In second control, after derivation of cumulative power transfer distribution factor (CPTDF) from power network, a CPTDF-based EV control is modeled toward an incentive-based congestion criteria management. The uncertainties of solar, wind, and load are captured by a hybrid chronological time-period clustering algorithm with Monte Carlo method. The performance of the proposed EV-SCUC model is tested on the IEEE 6-bus and the IEEE One and Two Area RTS-96 systems with integrated EV fleets. Under EV spatiotemporal constraints, results confirm that the system operator can decisively control commitment decisions, total cost, incentive payments, and pre/postcontingency congestion criteria under different EV control parameters and RES penetration deterministically or stochastically.

This paper presents a multi-stage expansion model for the co-planning of transmission lines, battery energy storage (ES), and wind power plants (WPP). High penetration of renewable energy sources (RES) is integrated into the proposed model concerning renewable portfolio standard (RPS) policy goals. The possibility of bundling existing transmission lines to uprate power flow capacity is considered. Renewable energy curtailment and load shedding are included in the model to assess the system operation more precisely. Battery ES devices are co-planned to defer transmission expansion and renewable management. To make the time complexity of the problem tractable and capture the uncertainties of load and RES in an hourly resolution, a chronological time-period clustering algorithm is used to extract the representative hours of each planning stage. Additionally, the flexible ramp reserve is utilized to handle the uncertainty of RES. An accelerated Benders dual decomposition (BDD) algorithm is developed to solve the proposed model mixed-integer linear programming (MILP) formulation. The N-1 security criterion is evaluated by considering a designed contingency screening (CS) algorithm to identify higher risk contingencies. The effectiveness of the proposed co-planning model is evaluated using IEEE RTS 24-bus and IEEE 118-bus test systems.

A virtual power plant (VPP) is a solution that brings distributed generation (DG) resources together and allows them to be optimally utilized to meet load demands in the presence of technical and pollution constraints. Electricity, heat, and natural gas are interdependent at the levels of generation, transmission, and consumption, and the interactions of these energy sources need to be considered. This paper presents an optimal model for daily operation of a multi-energy virtual power plant (MEVPP), including electric, thermal, and natural gas sectors. MEVPP includes small-scale gas-fired and non-gas-fired DGs, combined heat and power (CHP), power to gas (P2G), boilers, electrical storage, electric vehicles (EV), and thermal storage. Renewable energy resources (RES), including wind turbines (WT), photovoltaic (PV), and PV-thermal (PVT), also supply P2G technology. Smart grid technologies such as price-based demand response (PBDR) and incentive-based demand response (IBDR) are employed for electric loads. The proposed MEVPP model is eligible to participate in day-ahead electricity, natural gas, heat markets, and electrical spinning reserve market. The scheduling model is multi-objective to maximize MEVPP profit and minimize carbon dioxide emissions. The Epsilon constraint method is utilized to solve the problem, and the best Pareto point is chosen using the fuzzy satisfying approach.

Integration of smart grid technologies in distribution systems, particularly behind-the-meter initiatives, has a direct impact on transmission network planning. This paper develops a coordinated expansion planning of transmission and active distribution systems via a stochastic multistage mathematical programming model. In the transmission level, in addition to lines, sitting and sizing of utility-scale battery energy storage systems and wind power plants under renewable portfolio standard policy are planned. Switchable feeders and distributed generations are decision variables in the distribution level while the impact of demand response programs as a sort of behind-the-meter technologies is accommodated as well. Expansion of electric vehicle taxi charging stations is included as a feasible option in both transmission and distribution levels. In order to deal with short-term uncertainty of load demand, renewable energy sources output power, and the charging pattern of electric vehicle taxis in each station, a chronological time-period clustering algorithm along with Monte Carlo simulation is utilized. The proposed model is tackled by means of Benders Dual Decomposition (BDD) method. The IEEE RTS test system (as the transmission system) along with four IEEE 33-node test feeders (as distribution test systems) are examined to validate effectiveness of the proposed model.