In the current study, the optimal performance of regional market management (RMM) is presented in one specific area of the market in order to determine the optimal demand aiming to minimize the costs of smoothing the load curve, and to implement the demand response program (DRP). In addition, the attempt hasbeen made in this study to minimize the involuntary lost energy in terms of the initial price uncertainty and the technical constraints including the price fluctuation limits, demand ceilings and the relative risk limits. Furthermore, a market-based tensile model is presented in the form of a combination of the formulations of over lapping generations (OLG) and price elasticity (PEM) for determining demand levels in DRP. The informationgap decision theory (IGDT) is presented to model the initial price uncertainty in the above issue, according todifferent rates of time preference as well as the algorithm of the co-evolutionary particle swarm optimization (CPSO). IGDT risk-aversion and risk-taking strategies help RMM to select the best strategy with the desired risk level for controlling the price uncertainty, improving the load curve, enhancing the reliability and reducing the costs.

The growing use of distributed generation (DG) at the distribution level has led to a change in the status of distribution networks from a passive network to an active network such as transmission systems. Therefore, transmission network pricing method such as nodal pricing could be used in the distribution network.DG connection to the distribution network affects bus nodal pricing. If the DG presence reduces losses and congestion in the distribution network, nodal pricing will also decrease. This paper presents a method for calculating the optimal size and place of DG in the distribution network based on nodal pricing. This planning is done to maximize the profits of distribution companies that have used DG in their network to meet several advantages. The simulation was performed using the improved artificial bee colony algorithm (IABC). In theIABC algorithm, by exchanging the received information between bees according to Newton and gravity laws, it uses all this algorithm capacity to find the ideal answer by considering the constraints applied to the system. In most DG placement articles, network loads are assumed to be constant. Because loads are often sensitive to voltage and frequency, constant load analysis leads to inaccurate results. Therefore, in this paper, the proposed method is implemented on a 38-bus radial distribution system with a model of real loads sensitive to the voltage and frequency of the system, including residential, commercial, and industrial loads.