Placement of Combined Heat, Power and Hydrogen Production Fuel Cell Power Plants in a Distribution Network

Abstract

This paper presents a new Fuzzy Adaptive Modified Particle Swarm Optimization algorithm (FAMPSO) for the placement of Fuel Cell Power Plants (FCPPs) in distribution systems. FCPPs, as Distributed Generation (DG) units, can be considered as Combined sources of Heat, Power, and Hydrogen (CHPH). CHPH operation of FCPPs can improve overall system efficiency, as well as produce hydrogen which can be stored for the future use of FCPPs or can be sold for profit. The objective functions investigated are minimizing the operating costs of electrical energy generation of distribution substations and FCPPs, minimizing the voltage deviation and minimizing the total emission. In this regard, this paper just considers the placement of CHPH FCPPs while investment cost of devices is not considered. Considering the fact that the objectives are different, non-commensurable and nonlinear, it is difficult to solve the problem using conventional approaches that may optimize a single objective. Moreover, the placement of FCPPs in distribution systems is a mixed integer problem. Therefore, this paper uses the FAMPSO algorithm to overcome these problems. For solving the proposed multi-objective problem, this paper utilizes the Pareto Optimality idea to obtain a set of solution in the multi-objective problem instead of only one. Also, a fuzzy system is used to tune parameters of FAMPSO algorithm such as inertia weight. The efficacy of the proposed approach is validated on a 69-bus distribution system. OPEN ACCESS

Game Optimization Theory and Application in Distribution System Expansion Planning, Including Distributed Generation

“…It can find SNE through the analysis of the strategy space, and five schemes are compared in this paper, Scheme 1 is obtained by the traditional decision-making method, Scheme 2 to Scheme 5 correspond to different subjective preferences, and each scheme has specific Feeders upgrading schemes Feeders upgrading schemes characteristics. From the results of comparison, we could deduce that FMW could handle subjective preference flexibility and find SNE scientifically: Scheme 1: The final strategy is proposed by fuzzy method [26]; Scheme 2: The final strategy is proposed by FMW, the subjective preference is total costs; Scheme 3: The final strategy is proposed by FMW, the subjective preference is total losses; Scheme 4: The final strategy is proposed by FMW, the subjective preference is voltage profile; Scheme 5: The final strategy is proposed by FMW, there is no subjective preference among the objectives. Table 9 shows the performances in total costs among the five schemes, where Schemes 2, 3 and 5 are feeder upgrading schemes.…”

An efficient scenario-based stochastic programming for optimal planning of combined heat, power, and hydrogen production of molten carbonate fuel cell power plants

“…Thermal energy generated by MCFCPPs depends on its electrical energy production determined according to the following equation [15]:…”

“…In this procedure, a fuzzy membership function is chosen to determine the best compromised solution. For all the particles of the repository a membership function is defined for each of the objective functions as follows [15]:…”

“…[15]. The effects of employing DGs for simultaneous production of electricity and heat on energy management and operation efficiency increment are studied in Ref.…”

Optimal Location of IPFC for Improving Power System Performance Using Cuckoo Search Algorithm

“…1. It consists of at least two back-to-back DC-AC converters connected through a common DC link and the DC link between each VSC can be represented by bidirectional link, for exchanging active power between them [19,20]. The bus i,j and k has the complex voltages V i , V j , and V k and the series compensation of series converter voltage, Vse in is the controllable series injected voltage source which can be defined as …”