Fuzzy multiobjective model for distributed generation expansion planning in uncertain environment

Abstract

Uncertainty is one of the important factors that increase risk of exact decision makings. Although power systems behave more probabilistic today and system risk cannot be avoided completely, it can be evaluated and managed to an acceptable extent in planning, design, and operation activities. This paper presents a fuzzy multiobjective model for distributed generation planning so that uncertainties are modeled using fuzzy numbers (trapezoidal form). The proposed fuzzy model is based on the risk of economic, technical, and environmental objectives as well as fuzzy values of investment and operation cost of DG units due to technology's progress and fuel price fluctuations in the future. This model determines the optimal time, location, size, and type of DG units in distribution networks, using a multiobjective genetic algorithm (NSGA-II). The technologies which are considered in this study are photovoltaic (PV), wind turbine (WT), fuel cell (FC), microturbine (MT), gas turbine (GT), and diesel engine (DE). The proposed model is applied on a typical distribution system (IEEE 37 node test feeder) to assess the efficiency of the approach.

A fuzzy environmental-technical-economic model for distributed generation planning

“…is assumed fixed and 30% larger than the average market price (r w ) in Table 1. The cost of energy not served is assumed to vary at each node of the distribution system depending on its importance as given in [17]. Also, the unit upgrading cost of branch (C ub ) and upgrading cost of transformer (C ut ) are assumed 0.15 (M$/km) and 0.05 ($/kVA), respectively [5].…”

“…The network data for this system can be found in [19] and the information of DG technologies is presented in Tables 1 and 2 including capital costs, operating costs, capacity factors and emission rates. The discount and inflation rates are assumed to be 9% and 6%, respectively during the planning horizon [17]. The maximum installed capacity at each node is assumed 400 kW.…”

“…Due to the subjective imprecise nature of the decision maker's judgment, a fuzzy satisfying method is applied here to select the preferred solution among non-dominated solutions [17]. Through fuzzy set theory, each objective function is presented with a linear membership function.…”

Uncertain Reasoning of Distribution Network Planning for Distributed Generator

“…[5] introduces flow calculation, fuzzy power flow algorithm. A distribution network planning is carried out by constructing a fuzzy modeling capacity factor trapezoidal membership function of wind power in [6]. By taking into account load forecasting value and some types of DG output power uncertainty construct fuzzy expected value model in [7].…”

A Low-Carbon Dispatch Model in a Wind Power Integrated System Considering Wind Speed Forecasting and Energy-Environmental Efficiency

“…In MODM, the best solution does not exist according to the conventional concept of optimality. Thus, the subjectivity of the decision maker must be adequately considered to achieve solutions that meet subjective and objective requirements [41]. Fuzzy math is an effective way to handle uncertain information.…”