Optimal management of microgrids including renewable energy scources using GPSO-GM algorithm

Environmentally friendly energy sources with high power quality or reliability and low costs are regarded as an effective solution for energy supply problems arising from use of conventional methods. Presented in this paper, gives an optimal management strategy of PV/wind/diesel independent hybrid systems for supplying required energy in autonomous microgrids. A new optimization problem is formulated for minimizing the capital investment and fuel costs of the system. To solve the proposed optimization problem a novel algorithm, named Guaranteed convergence Particle Swarm Optimization with Gaussian Mutation (GPSO-GM), is developed. Two operators, namely mutation and guaranteed convergence, are added to PSO in order to help finding more accurate results and increasing the speed of calculations. The performance of the proposed strategy is evaluated in two case studies.     

Optimal operation of microgrid using hybrid differential evolution and harmony search algorithm

This paper proposes the generation scheduling approach for a microgrid comprised of conventional generators, wind energy generators, solar photovoltaic (PV) systems, battery storage, and electric vehicles. The electrical vehicles (EVs) play two different roles: as load demands during charging, and as storage units to supply energy to remaining load demands in the MG when they are plugged into the microgrid (MG). Wind and solar PV powers are intermittent in nature; hence by including the battery storage and EVs, the MG becomes more stable. Here, the total cost objective is minimized considering the cost of conventional generators, wind generators, solar PV systems and EVs. The proposed optimal scheduling problem is solved using the hybrid differential evolution and harmony search (hybrid DE-HS) algorithm including the wind energy generators and solar PV system along with the battery storage and EVs. Moreover, it requires the least investment.     

Modeling and operation optimization of RE integrated microgrids considering economic,energy, and environmental aspects

Microgrids provide promising solution for integration of renewable energy sources in the electrical grid. To exploit the key benefits, achieving the economical operation of renewable aided microgrids has become necessary and is a challenging task. This paper presents an efficient optimization model to minimize the operational cost of a solar integrated microgrid. We formulate a joint optimization mixed integer problem for appropriate modeling of the system under various practical constraints. An efficient solution is obtained with a distributed approach such that the original problem is solved in two stages. Dual decomposition approach is adopted for cost, emissions, and solar share optimization. Lagrange relaxation, Lambda iteration method, and binary integer programming are employed to obtain the joint optimization solution. Finally, the performance of the proposed model is validated through simulations that show that an overall cost reduction of 4.2070e+04 $ and emission reduction of 7.2001e+03 kg are achieved with the proposed model.     

An efficient algorithm for multi-objective optimal operation management of distribution network considering fuel cell power plants

This paper presents an interactive fuzzy satisfying method based on Hybrid Modified Honey Bee Mating Optimization (HMHBMO). Its purpose is to solve the Multi-objective Optimal Operation Management (MOOM) problem which can be affected by Fuel cell power plants (FCPPs). Minimizing total electrical energy losses, total electrical energy cost, total pollutant emission produced by sources and deviation of bus voltages are the objective functions in this method. A new interactive fuzzy satisfying method is presented to solve the multi-objective problem by assuming that the decision-maker (DM) has fuzzy targets for each of the objective functions. Through the interaction with the DM, the fuzzy goals are quantified by eliciting the corresponding membership functions. Considering the current solution, the DM updates the reference membership values until the best solution can be obtain. The MOOM problem is modeled as a mixed integer nonlinear programming problem. Therefore, evolutionary methods can be used to solve this problem since they are independence of objective function’s type and constraints. Recently researchers have presented a new evolutionary method called Honey Bee Mating Optimizations (HBMO) algorithm. Original HBMO often converges to local optima and this is a disadvantage of this method. In order to avoid this shortcoming we propose a new method. This method improves the mating process and also combines the modified HBMO with a Chaotic Local Search (CLS). Numerical results on a distribution test system have been presented to illustrate the performance and applicability of the proposed method.     

Combined heat and power economic dispatch problem using the invasive weed optimization algorithm

Cogeneration units which produce both heat and electric power are found in many process industries. These industries also consume heat directly in addition to electricity. The cogeneration units operate only within a feasible zone. Each point within the feasible zone consists of a specific value of heat and electric power. These units are used along with other units which produce either heat or power exclusively. Hence the economic dispatch problem for these plants optimizing the fuel cost is quite complex and several classical and meta-heuristic algorithms have been proposed earlier. This paper applies the invasive weed optimization algorithm which is inspired by the ecological process of weed colonization and distribution. The results obtained have been compared with those obtained by other methods earlier and showed a marked improvement over earlier ones.     

Development of a combined approach for improvement and optimization of karanja biodiesel using response surface methodology and genetic algorithm

This paper described the production of karanja biodiesel using response surface methodology (RSM) and genetic algorithm (GA). The optimum combination of reaction variables were analyzed for maximizing the biodiesel yield. The yield obtained by the RSM was 65% whereas the predicted value was 70%. The mathematical regression model proposed from the RSM was coupled with the GA. By using this technique, 90% of the yield was obtained at a molar ratio of 38, a reaction time of 8 hours, a reaction temperature of 40 oC, a catalyst concentration of 2% oil, and a mixing speed of 707 r/min. The yield produced was closer to the predicted value of 94.2093%. Hence, 25% of the improvement in the biodiesel yield was reported. Moreover the different properties of karanja biodiesel were found closer to the American Society for Testing & Materials (ASTM) standard of biodiesel.     

Should a small combined heat and power plant (CHP) open to its regional power and heat networks? Integrated economic,energy, and emergy evaluation of optimization plans for Jiufa CHP

The development of industrial ecology has led company managers to increasingly consider their company's niche in the regional system, and to develop optimization plans. We used emergy-based, ecological-economic synthesis to evaluate two optimization plans for the Jiufa Combined Heat and Power (CHP) Plant, Shandong China. In addition, we performed economic input–output analysis and energy analysis on the system. The results showed that appropriately incorporating a firm with temporary extra productivity into its regional system will help maximize the total productivity and improve ecological-economic efficiency and benefits to society, even without technical optimization of the firm itself. In addition, developing a closer relationship between a company and its regional system will facilitate the development of new optimization opportunities. Small coal-based CHP plants have lower-energy efficiency, higher environmental loading, and lower sustainability than large fossil fuel and renewable energy-based systems. The emergy exchange ratio (EER) proved to be an important index for evaluating the vitality of highly developed ecological-economic systems.