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.@en

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.@en

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.@en

The growing penetration of renewable energy sources (RESs) is inevitable to reach net zero emissions. In this regard, optimal planning and operation of power systems are becoming more critical due to the need for modeling the short-term variability of RES output power and load demand. Considering hourly time steps of one or more years to model the operational details in a long-term expansion planning scheme can lead to a practically unsolvable model. Therefore, a clustering-based hybrid time series aggregation algorithm is proposed in this paper to capture both extreme values and temporal dynamics of input data by some extracted representatives. The proposed method is examined in a complex co-planning model for transmission lines, wind power plants (WPPs), short-term battery and long-term pumped hydroelectric energy storage systems. The effectiveness of proposed mixed-integer linear programming (MILP) model is evaluated using a modified 6-bus Garver test system. The simulation results confirm the proposed model efficacy, especially in modeling long-term energy storage systems.@en

In this paper, an integrated multi-period model for long term expansion planning of electric energy transmission grid, power generation technologies, and energy storage devices is introduced. The proposed method gives the type, size and location of generation, transmission and storage devices to supply the electric load demand over the planning horizon. The sitting and sizing of Battery Energy Storage (BES) devices as flexible options is addressed to cover the intermittency of Renewable Energy Sources (RESs), mitigate lines congestion, and postpone the need for new transmission lines and power plants installation. For efficient handling of RESs uncertainties, and operational flexibility, the upward and downward Flexible Ramp Spinning Reserve (FRSR) are modeled. Besides, the Low-Carbon Policy (LCP) is considered in the objective function of the proposed Transmission, Generation, and Storage Expansion Planning (TGSEP) model. A hierarchical clustering method that can preserve the chronology of input time series throughout the planning horizon periods is developed to capture the short-term uncertainties of load demand and RESs. The short-term operational flexibility requirements make the joint long-term transmission and generation planning a high computational problem. Therefore, the Mixed-Integer Linear Programming (MILP) formulation of the model is solved using an accelerated Benders Dual Decomposition (BDD) method. The IEEE RTS test system is utilized to validate the effectiveness of the proposed joint expansion planning model.@en

In this article, a multistage model for expansion coplanning of transmission lines, battery energy storages, and wind farms (WFs) is presented considering resilience against extreme weather events. In addition to high-voltage alternating current lines, multiterminal voltage source converter based high-voltage direct current lines are planned to reduce the impact of high-risk events. To evaluate the system resilience against hurricanes, probable hurricane speed scenarios are generated using Monte Carlo simulation. The fragility curve concept is utilized for calculating the failure probability of lines due to extreme hurricanes. Based on each hurricane damage, the probable scenarios are incorporated in the proposed model. Renewable portfolio standard policy is modeled to integrate high penetration of WFs. To deal with the wind power and load demand uncertainties, a chronological time-period clustering algorithm is introduced for extracting representative hours in each planning stage. A deep learning approach based on bidirectional long short-term memory networks is presented to forecast the yearly peak loads. The mixed-integer linear programming formulation of the proposed model is solved using a Benders decomposition algorithm. A modified IEEE RTS test system is used to evaluate the proposed model effectiveness. @en

In this article, a multistage model for expansion coplanning of transmission lines, battery energy storages, and wind farms (WFs) is presented considering resilience against extreme weather events. In addition to high-voltage alternating current lines, multiterminal voltage source converter based high-voltage direct current lines are planned to reduce the impact of high-risk events. To evaluate the system resilience against hurricanes, probable hurricane speed scenarios are generated using Monte Carlo simulation. The fragility curve concept is utilized for calculating the failure probability of lines due to extreme hurricanes. Based on each hurricane damage, the probable scenarios are incorporated in the proposed model. Renewable portfolio standard policy is modeled to integrate high penetration of WFs. To deal with the wind power and load demand uncertainties, a chronological time-period clustering algorithm is introduced for extracting representative hours in each planning stage. A deep learning approach based on bidirectional long short-term memory networks is presented to forecast the yearly peak loads. The mixed-integer linear programming formulation of the proposed model is solved using a Benders decomposition algorithm. A modified IEEE RTS test system is used to evaluate the proposed model effectiveness. @en

In this paper, a dynamic (i.e., multi-year) hybrid model is presented for Transmission Expansion Planning (TEP) utilizing the High Voltage Alternating Current (HVAC) and multi terminal Voltage Sourced Converter (VSC)-based High Voltage Direct Current (HVDC) alternatives. In addition to new HVAC and HVDC lines, the possibility of converting existing HVAC transmission lines to HVDC lines is considered in the proposed model. High shares of renewable resources are integrated into the proposed hybrid AC/DC TEP model. Due to the intermittency of renewable resources, the planning of large-scale Energy Storage (ES) devices is considered. In order to accurately estimate the total TEP costs and hence capturing the scenarios of load and renewable generation uncertainty, using a clustering approach, each year of the planning horizon is replaced with four representative days. The proposed model is formulated as a Mixed-Integer Linear Programming (MILP) problem. Using Benders Decomposition (BD) algorithm, the proposed model is decomposed into a Master investment problem to handle the decision variables, and Sub-problems to check the feasibility of master problem solution and optimize the operation and ES investment cost. Three test systems are used as case studies to demonstrate the effectiveness of the proposed hybrid AC/DC TEP model.@en

In this paper, a Mixed Integer Linear Programming (MILP) model for dynamic Transmission Expansion Planning (TEP) is presented which is able to consider the impacts of Energy Storage (ES) devices under growing penetration of Renewable Energy Sources (RESs). The most important advantages of ES devices are improving RESs accommodation, mitigation of line overflow, and reducing the investment cost of transmission lines. To model the short-term uncertainties of wind power and load, a hierarchical clustering method considering time dependent parameter chronology is utilized. In the proposed model, a linear DC Optimal Power Flow (DC-OPF) with the capability to calculate hourly generation costs, is formulated. The IEEE RTS 24-Bus test system is studied to validate the effectiveness of the proposed model. The results confirm that considering ES devices in TEP problem, especially in the presence of RESs considering short-term uncertainties, can be beneficial.@en

In this paper, a Mixed Integer Linear Programming (MILP) model for dynamic Transmission Expansion Planning (TEP) is presented which is able to consider the impacts of Energy Storage (ES) devices under growing penetration of Renewable Energy Sources (RESs). The most important advantages of ES devices are improving RESs accommodation, mitigation of line overflow, and reducing the investment cost of transmission lines. To model the short-term uncertainties of wind power and load, a hierarchical clustering method considering time dependent parameter chronology is utilized. In the proposed model, a linear DC Optimal Power Flow (DC-OPF) with the capability to calculate hourly generation costs, is formulated. The IEEE RTS 24-Bus test system is studied to validate the effectiveness of the proposed model. The results confirm that considering ES devices in TEP problem, especially in the presence of RESs considering short-term uncertainties, can be beneficial.@en

This paper develops a new model for optimal placement of a combination of single-phase and three-phase Micro Phasor Measurement Units (μPMUs) in smart distribution networks with radial asymmetrical structure for complete observability. The presented model is formulated as a Binary Integer Linear Programing (BILP) problem. In order to take the full advantage of the zero injection properties to make distribution network observable, two types of Zero Injection Buses (ZIBs) are taken into account including Fully Zero Injection Buses (FZIBs) and Partially Zero Injection Buses (PZIBs). With regard to the location of a FZIB or a PZIB in the radial distribution network, an innovative technique is applied to reduce the number of μPMUs. In addition, the single μPMU outage contingency is incorporated in the model. The suggested model is examined on asymmetrical IEEE 13-bus and 37-bus and 123-bus test feeders for different cases and the obtained results are analyzed to confirm the presented model.@en

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.@en

Electric demand and renewable power are highly variable, and the solution of a planning model relies on capturing this variability. This paper proposes a hybrid multi-area method that effectively captures both the intraday and interday chronology of real data considering extreme values, using a limited number of representative days, and time points within each day. An optimization-based representative extraction method is proposed to improve intraday chronology capturing. It ensures higher precision in preserving data chronology and extreme values than hierarchical clustering methods. The proposed method is based on a piecewise linear demand and supply representation, which reduces approximation errors compared to the traditional piecewise constant formulation. Additionally, sequentially linked day blocks with identical representatives, created through a mapping process, are employed for interday chronology capturing. To evaluate the efficiency of the proposed method, a comprehensive expansion co-planning model is developed, including transmission lines, energy storage systems, and wind farms.@en

In this paper, a game framework for defending the power system against intelligent physical attacks is modeled by investigating the impacts of Energy Storages (ESs). For this modeling, Optimal Placement of Energy Storage (OPES) with regard to two main cases is investigated. In the first case, it is assumed that just one transmission lines are attacked. This case is equal to making the power system N-1 secure using OPES. In the second case, it is assumed that two transmission line are attacked. Therefore, this case is equal to making the power system N-2 secure using OPES. Moreover, two proposed Algorithms are implemented for limited budget allocation to the power system protection and recovery. A five-bus system as a common case study system is considered for evaluation of the presented model. The obtained results justify the effectiveness of considering OPES for defending the power system against targeted physical attacks.@en

In this paper, a game framework for defending the power system against intelligent physical attacks is modeled by investigating the impacts of Energy Storages (ESs). For this modeling, Optimal Placement of Energy Storage (OPES) with regard to two main cases is investigated. In the first case, it is assumed that just one transmission lines are attacked. This case is equal to making the power system N-1 secure using OPES. In the second case, it is assumed that two transmission line are attacked. Therefore, this case is equal to making the power system N-2 secure using OPES. Moreover, two proposed Algorithms are implemented for limited budget allocation to the power system protection and recovery. A five-bus system as a common case study system is considered for evaluation of the presented model. The obtained results justify the effectiveness of considering OPES for defending the power system against targeted physical attacks.@en

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.@en