Market-based Optimal Power Flow Research Articles

Deterministic approach for active distribution networks planning with high penetration of wind and solar power

In this paper, a novel deterministic approach for the planning of active distribution networks within a distribution market environment considering multi-configuration of wind turbines (WTs) and photovoltaic (PV) cells is proposed. Multi-configuration multi-period market-based optimal power flow is utilized for maximizing social welfare taking into account uncertainties associated with wind speed, solar irradiance and load demand as well as different operational status of WTs and PVs. Multi-period scenarios method is exploited to model the aforementioned uncertainties. The proposed approach assesses the effect of multiple-configuration of PVs and WTs on the amount of wind and solar power that can be produced, the distribution locational marginal prices all over the network and on the social welfare. The application of the proposed approach is examined on a 30-bus radial distribution network.

Active distribution networks planning with high penetration of wind power

In this paper, a stochastic method for active distribution networks planning within a distribution market environment considering multi-configuration of wind turbines is proposed. Multi-configuration multi-scenario market-based optimal power flow is used to maximize the social welfare considering uncertainties related to wind speed and load demand and different operational status of wind turbines (multiple-wind turbine configurations). Scenario-based approach is used to model the abovementioned uncertainties. The method evaluates the impact of multiple-wind turbine configurations and active network management schemes on the amount of wind power that can be injected into the grid, the distribution locational marginal prices throughout the network and on the social welfare. The effectiveness of the proposed method is demonstrated with 16-bus UK generic distribution system. It was shown that multi-wind turbine configurations under active network management schemes, including coordinated voltage control and adaptive power factor control, can increase the amount of wind power that can be injected into the grid; therefore, the distribution locational marginal prices reduce throughout the network significantly.

Evaluating the Benefits of Optimal Allocation of Wind Turbines for Distribution Network Operators

This paper proposes a hybrid optimization method for optimal allocation of wind turbines (WTs) that combines a fast and elitist multiobjective genetic algorithm (MO-GA) and the market-based optimal power flow (OPF) to jointly minimize the total energy losses and maximize the net present value associated with the WT investment over a planning horizon. The method is conceived for distributed-generator-owning distribution network operators to find the optimal numbers and sizes of WTs among different potential combinations. MO-GA is used to select, among all the candidate buses, the optimal sites and sizes of WTs. A nondominated sorting GA II procedure is used for finding multiple Pareto-optimal solutions in a multiobjective optimization problem, while market-based OPF is used to simulate an electricity market session. The effectiveness of the method is demonstrated with an 84-bus 11.4-kV radial distribution system.

A Model for Wind Turbines Placement Within a Distribution Network Acquisition Market

This paper proposes an innovative exhaustive search method for the optimal placement of wind turbines (WTs) in electrical distribution systems taking into account wind speed and load demand uncertainty, and the variability of electrical energy prices within a distribution network operator (DNO) acquisition market environment. The method combines Monte Carlo simulation (MCS) and market-based optimal power flow (OPF) to maximize the net present value (NPV) related to the investment made by WTs' developers over a planning horizon. In particular, the MCS data feed the market-based OPF problem with inter-temporal constraints in order to find the most convenient WTs allocation and priority on the network, based on distribution-locational marginal prices (D-LMPs) in a competitive electricity market. The effectiveness of the proposed method is demonstrated with an 84-bus 11.4-kV radial distribution system.

Strategic placement of distribution network operator owned wind turbines by using market‐based optimal power flow

In this study, a new methodology to optimally allocate wind turbines (WTs) in distribution networks is proposed. A market-based optimal power flow is used to determine the optimal numbers and capacities of WTs in a way that maximises the social welfare. The method is conceived for distribution network operators to strategically allocate WTs in distribution networks. The proposed method by yielding location-specific WTs capacity settlement both in terms of cost reduction and consumers' benefits is consistent with distribution network topology and constraints. The method is solved by using step-controlled primal dual interior point method considering network constraints. The effectiveness of the proposed method is demonstrated with two radial distribution systems including an 84-bus 11.4 kV and a 69-bus 11 kV network.

Assessing Wind Turbines Placement in a Distribution Market Environment by Using Particle Swarm Optimization

A hybrid optimization method for optimal allocation of wind turbines (WTs) that combines particle swarm optimization (PSO) and market-based optimal power flow (OPF) with security constraints is proposed in this paper. The method maximizes the net present value (NPV) associated with WTs investment in a distribution market environment. The PSO is used to choose the optimal size while the market-based OPF to determine the optimal number of WTs at each candidate bus. The stochastic nature of both load demand and wind power generation is modeled by hourly time series analysis considering different combinations of wind generation and load demand. The effectiveness of the method is demonstrated with an 84-bus 11.4-kV radial distribution system.

Probabilistic Assessment of the Impact of Wind Energy Integration Into Distribution Networks

Combined Monte Carlo simulation (MCS) and market-based optimal power flow (OPF) considering different combinations of wind generation and load demand over a year are used to evaluate wind turbines (WTs) integration into distribution systems. MCS is used to model the uncertainties related to the stochastic variations of wind power generation and load demand while the social welfare is maximized by means of market-based OPF with inter-temporal constraints. The proposed probabilistic methodology allows evaluating the amount of wind power that can be injected into the grid as well as the impact of wind power penetration on the social welfare and on distribution-locational marginal prices. Market-based OPF is solved by using step-controlled primal dual interior point method considering network constraints. The effectiveness of the proposed probabilistic method in assessing the impact of wind generation penetration in terms of both technical and economic effects is demonstrated with an 84-bus 11.4-kV radial distribution system.

Optimal wind turbines placement within a distribution market environment

This paper proposes a hybrid optimization method for optimal allocation of wind turbines (WTs) that combines genetic algorithm (GA) and market-based optimal power flow (OPF). The method jointly maximizes net present value (NPV) related to WTs investment made by WTs’ developers and social welfare (SW) considering different combinations of wind generation and load demand over a year. The GA is used to choose the optimal size while the market-based OPF to determine the optimal number of WTs at each candidate bus. The stochastic nature of both load demand and wind power generation is modeled by hourly time series analysis. The effectiveness of the method is demonstrated with an 84-bus 11.4kV radial distribution system.

Combined Monte Carlo simulation and OPF for wind turbines integration into distribution networks

In this paper, a probabilistic method to evaluate the impact of wind turbines (WTs) integration into distribution systems within a market environment is proposed. Combined Monte Carlo simulation (MCS) technique and market-based optimal power flow (OPF) are used to maximize the social welfare considering different combinations of wind generation and load demand over a year. MCS is used to model the uncertainties related to the stochastic nature of wind and the volatility of WTs’ offers. The market-based OPF is solved by using step-controlled primal dual interior point method considering network constraints. The method is conceived for distribution network operators (DNOs) in order to evaluate the effect of WTs integration into the grid. The effectiveness of the proposed method is demonstrated with an 84-bus 11.4kV radial distribution system.

Evaluating the integration of wind power into distribution networks by using Monte Carlo simulation

This paper provides a probabilistic method to assess the impact of wind turbines (WTs) integration into distribution networks within a market environment. Combined Monte Carlo simulation (MCS) technique and market-based optimal power flow (OPF) are used to maximize the social welfare by integrating demand side management (DSM) scheme considering different combinations of wind generation and load demand over a year. MCS is used to model the uncertainties related to the stochastic variations of wind power generation and load demand. The market-based OPF is solved by using step-controlled primal dual interior point method considering network constraints. The method is conceived for distribution network operators (DNOs) in order to evaluate the effect of WTs integration into the network. The effectiveness of the proposed method is demonstrated with an 84-bus 11.4kV radial distribution system.

On Computational Issues of Market-Based Optimal Power Flow

The deregulated electricity market calls for robust optimal power flow (OPF) tools that can provide a) deterministic convergence; b) accurate computation of nodal prices; c) support of both smooth and nonsmooth costing of a variety of resources and services, such as real energy, reactive energy, voltages support, etc.; d) full active and reactive power flow modeling of large-scale systems; and e) satisfactory worst-case performance that meets the real-time dispatching requirement. Most prior research on OPF has focused on performance issues in the context of regulated systems, without giving much emphasis to requirements a)-c). This paper discusses the computational challenges brought up by the deregulation and attempts to address them through the introduction of new OPF formulations and algorithms. Trust-region- based augmented Lagrangian method (TRALM), step-controlled primal-dual interior point method (SCIPM), and constrained cost variable (CCV) OPF formulation are proposed. The new formulations and algorithms, along with several existing ones, are tested and compared using large-scale power system models.