Distribution LOCATIONAL MARGINAL pricing (DLMP) is an efficient approach to optimize the pricing of distribution systems. This paper focuses on DLMP to minimize losses within the distribution network. This approach can be strategically manipulated to adjust the profits for distributed generation (DG) owners and the distribution company. Furthermore, the paper employs the information gap decision theory (IGDT) method scenarios to model the uncertainty surrounding electricity market PRICEs. By incorporating the risk-averse (RA) scenario, network operators can discern RA solutions and optimal outcomes derived from the algorithm. On the other hand, the risk-tolerance (RT) scenario helps identify riskier solutions, enabling appropriate decision-making based on whether the solutions are RA or risky in nature. To further enhance the quality of outcomes, this paper combines IGDT scenarios with the cheetah hunter optimization (CHO) algorithm to ensure the obtained results are both optimal and accurate. The proposed method’s performance is evaluated through simulations conducted on a 69-bus IEEE power network using the MATLAB software environment. The results obtained from this approach demonstrate its superior accuracy when compared to previous methodologies.

Abstract: This paper proposes an index for nodal market power detection in power market under LOCATIONAL MARGINAL Pricing (LMP). This index is an ex-ante technique to detect the market power. More precisely, this criterion detects the potential of exercising market power regardless of detecting the actual market power. Also it is obvious that pricing and market clearing method affect the potential of exercising market power. Different potential of market power exists in different pricing methods. This index has been analyzed under LMP method which seems to be a desirable environment to exercise market power. In LMP method by load growth, in some determined load levels which is called Critical Load Levels (CLLs), LOCATIONAL MARGINAL PRICEs have step change. This step change in LOCATIONAL MARGINAL PRICEs causes step change in revenue and benefit of Gencos. So it is significant to detect the behavior of Gencos in the CLLs. The proposed criterion has been tested on constant system load and CLLs for two test system.

Transmission expansion planning (TEP) is one of the most important parts of expansion planning in power systems. Competition in these systems has resulted in essential changes in TEP models and criteria. While methods introduced so far are essentially based on dc load flow, the proposed method utilizes the ac optimal power flow (OPF) in order to model the real world condition and obtaining optimal plans. Furthermore, in order to model the operation and investment costs, transmission tariffs are used. Investigation on 8-bus system confirms the advantages of the proposed method as compared with previously presented approaches.

Expansion planning strategies in power systems have been seriously influenced by disintegration of these systems during the past decade. Transmission Expansion Planning (TEP) is one of the most important parts of expansion planning of power systems. In this paper, a new algorithm for TEP in these environments is presented. The method is based on PROBABILISTIC LOCATIONAL MARGINAL PRICE (LMP) considering electrical losses, transmission tariffs and transmission congestion costs.Incorporating modeling of reactive power and uncertainty of generation and load are other important features of the proposed algorithm.

LOCATIONAL MARGINAL Pricing (LMP) is arguably the most effective and commonly employed mechanism to provide the most reliable economic signal to market participants. Meanwhile, nodal PRICEs depend on active power losses and transmission congestion which may be affected by harmonics pollution. In the conventional method, power system and loads are assumed linear and nodal PRICEs are obtained by results of optimal power flow (OPF) at the power frequency. Harmonics lead to skin effect and increasing loss. Further, harmonic flowing in branches in a power network occupies transmission capacity. For providing more accurate signals to market participants and achieving more accurate nodal PRICEs, harmonic effects on LMP are investigated and a framework is developed for LMP calculation in a harmonic polluted power system. In this framework, skin effect, losses, and congestion which can be arisen by harmonic pollution are modelled in optimal power flow (OPF) and are considered in LMP calculation. The proposed concept is implemented with 9-bus and 30-bus test systems while nodal PRICE changes are also indicated.

In restructured systems, LOCATIONAL MARGINAL PRICEs are highly increased in load peak and short circuit durations. Uses of energy management programs such as demand response program are one of the tools that can help to independent system operator for LOCATIONAL MARGINAL PRICEs’ control. In this paper, optimal implementation of emergency demand response program is considered for LOCATIONAL MARGINAL PRICEs control. Also, imperialist competition algorithm is used to optimize the proposed PRICEs for consumers that are connected to different buses for both peak and emergency states that lead to minimize the cost of system and the maximum of public benefit arising from the implementation of the emergency demand response program. The curve of New York regional load and IEEE 14 buses test system is used to test the proposed approach. The results are comprised with each other in two modes: without implementing energy management programs and when the proposed PRICE is the same for all consumers.

The topic of distribution network reconfiguration has been recognized as a common problem in the operation of distribution networks. Up to now, reconfiguration of the distribution network has been used to reduce power loss, issues with reliability and emergency problems. As a result of the increasing installation rate of Distributed Generations (DGs) and the increasingly increasing number of Electric Vehicles (EVs), distribution network operators have faced new problems as a result of high sensitive loads. These new facets of the problem of network operation consist of the problem of congestion and enable the business mechanism to be executed by an existing distribution market. With the application of Distribution LOCATIONAL MARGINAL PRICE (DLMP), using Particle Swarm Optimization (PSO), this paper introduces a new methodology to solve this problem. As a suitable case study, the standard IEEE 33-bus network has been selected and simulation results show the compatibility of the proposed system. In the case of not considering DGs and the problem of congestion, comparisons with other papers were also discussed. The participation of DGs in the electricity distribution industry, the issue of congestion and the numerical results have been shown and discussed below.

Restructuring of power system has faced this industry with numerous uncertainties. As a result, transmission expansion planning (TEP) like many other problems has become a very challenging problem in such systems. Due to these changes, various approaches have been proposed for TEP in the new environment. In this paper a new algorithm for TEP is presented. The method is based on PROBABILISTIC LOCATIONAL MARGINAL PRICE (LMP) considering electrical loss, transmission tariffs, and transmission congestion costs. It also considers the load curtailment cost in LMP calculations. Furthermore, to emphasize on competence of competition ability of the system, the final planes) is (are) selected based on minimization of average of total congestion cost for transmission system.