Optimal siting and sizing of renewable energy-based distributed generation in distribution systems considering CO2 emissions

Renewable energy-based distributed generation (DG) is critical to reducing the environmental impact of the electricity industry. However, poor planning of these devices can have a negative impact on network operation, which can vary depending on the location, size, and type of generator installed. Thus this chapter addresses the optimal siting and sizing of renewable energy-based DG, considering CO2 emissions limits to promote an efficient, sustainable, and environmentally friendly distribution network. The planning problem is solved using a dynamic approach in which the planning horizon is divided into multiple stages to determine when investments in DG should be made to meet the increase in electricity demand as efficiently as possible. The objective is to minimize the total cost, which includes both investment and operating costs while meeting the physical and operational constraints of the network. Furthermore, CO2 emission limits are considered to create an environmentally friendly investment plan. For this purpose, renewable generation, both dispatchable and nondispatchable, is considered within the investment options. Uncertainties associated with electricity demand, the price of energy purchased at the substation, and nondispatchable DG are addressed through scenario-based stochastic optimization. The resulting model is a mixed-integer nonlinear programming problem that is reformulated into a mixed-integer linear programming (MILP) formulation using appropriate linearization techniques. This MILP model was written in the mathematical language AMPL, and the commercial solver CPLEX was used to find its solution. The model was tested using a 134-node distribution system, and the results demonstrate its effectiveness and applicability in determining the best location, size, and optimal combination of the types of generators that must be installed to achieve an efficient and environmentally friendly distribution network.

Keywords

Active distribution networks, mixed-integer linear programming, multistage programming, renewable energy-based distributed generation, stochastic programming