A stochastic mixed-integer conic programming model for distribution system expansion planning considering wind generation

Abstract

This paper presents a stochastic scenario-based approach to finding an efficient plan for the electrical power distribution systems. In this paper the stochasticity for the distribution system expansion planning (DSEP) problem refers to the loads and wind speed behavior. The proposed DSEP model consist the expansion and/or construction of new substations, installation of new primary feeders and/or reinforcement the existing, installation of wind-distributed generation based, reconfiguration of existing network, and the proposed DSEP is solved considering uncertainty in electric demand and distributed generation. In this regard, a two-stage stochastic programming model is used, wherein the first stage the investment decision is made and the second stage calculates the expected operating value which depends on the stochastic scenarios. The mathematical approach is based on a mixed integer conic programming (MICP) model. By using this MICP model and a commercial optimization solver, finding the optimal global solution is guaranteed. Moreover, in this paper by using the B Juan Manuel Home Ortiz

Key Issues on the Design of an Offshore Wind Farm Layout and Its Equivalent Model

“…The placement of wind turbines could also refer to other optimization algorithms that have been applied to other energy-related areas, such as the distribution system expansion planning [35] and the allocation of storage devices and renewable generators [36]. In reference [35], the planning of the electrical power distribution systems was investigated by considering the variation and uncertainty from renewable energy sources, and a two-stage stochastic programming model was used; additionally, different techniques about the optimization were also compared. Reference [36] developed a mixed integer conic programming model to obtain the optimal size and location of distributed generators in a distribution system; the developed model considered the uncertainty from renewable energy sources into its decision-making.…”

Optimal Management of an Energy Storage Unit in a PV-Based Microgrid Integrating Uncertainty and Risk

“…Subject to (P GD t C GD t (22) in which w represents the weighting factor for concerning the risk, P PV t represents the total generated power from PV, and D t is the power demand at each time step t. The constraint (17) specifies that the PV's overall produced power is directed to the demand of the load. The constraint represented by Equation (18) represents the power demand which is entirely provided by the grid, ESS, or PV. S max and S min represent the maximum and minimum admissible charge level of the ESS and the constraints (19) and (20) and indicate that the amount of storage of the ESS in the succeeding time step continues larger than S min and smaller than S max , respectively.…”

“…This may result in operating the power system near the maximum loading point [13] that, by increasing the negative impacts of end-users on producing emissions [14,15] and endangering the voltage stability, decreases the system flexibility [16]. As a consequence, several requirements became necessary for a flexible power system due to the expansion and use of renewable energy sources in an effective way [17,18]. This will lead to an increase in microgrids with the purpose of enabling bidirectional power flow between power generation and final customers which is expected to play a key role in future power networks.…”

A Mixed Integer Conic Model for Distribution Expansion Planning: Matheuristic Approach

“…Therefore, finding a solution for large-scale systems via classical optimization techniques is almost impossible or practically unviable, due to the high computational time which is required for finding a highquality or even a feasible solution. Viable methodologies that can handle the medium-and large-scale DSEP problems with high computational efficiency can be categorized into heuristic-based algorithms and joint heuristic-based and classical techniques [20]- [25]. The authors in [20], [21] proposed an evolutionary PSO to find the optimal investment in new equipment for the network while the uncertainties in demand and energy costs are treated with Monte Carlo simulation (MCS).…”

“…In [24], the model corresponds with the reinforcement of the existing network, and installation of RES and intelligent meters considering the demand response program and CO2 reduction was handled via a joint GA and interior point method. A tabu search algorithm was used in [25] to solve the network expansion problem with the installation of wind-based DG while the uncertainties were addressed via a scenario-based stochastic programming model, while the operational state of the network was determined via a conic programming model.…”