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Towards Multi-Objective Optimization of Cost and Power Loss for Optimal Power Flow in Smart Grid
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An improved multi-objective multi-verse optimization (IMOMVO) algorithm is proposed for solving multi-objective optimal power flow (MOOPF) problem with uncertain renewable energy sources (RESs). Cross pollination steps of flower pollination algorithm (FPA) along with crowding distance and non-dominating sorting approach is incorporated with the basic MOMVO algorithm to further enhance the exploration, exploitation and for well distributed Pareto-optimal solution. To confirm the effectiveness of the proposed IMOMVO algorithm, modified IEEE 30-bus system with three cases are implemented by considering the total generation cost minimization, active power losses, and emission. The simulation results obtained with IMOMVO is compared with MOMVO, NSGA-II, and MOPSO, which reveal the capability of the proposed IMOMVO in terms of solution optimality and distribution.
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Demand Response Management (DRM) is considered one of the crucial aspects of the smart grid as it helps to lessen the production cost of electricity and utility bills. DRM becomes a fascinating research area when numerous utility companies are involved and their announced prices reflect consumer’s behavior. This paper discusses a Stackelberg game plan between consumers and utility companies for efficient energy management. For this purpose, analytical consequences (unique solution) for the Stackelberg equilibrium are derived. Besides this, this paper presents a distributed algorithm which converges for consumers and utilities. Moreover, different power consumption activities on the basis of time series are becoming a basic need for load prediction in smart grid. Load forecasting is taken as the significant concerns in the power systems and energy management with growing technology. The better precision of load forecasting minimizes the operational costs and enhances the scheduling of the power system. The literature has discussed different techniques for demand load forecasting like neural networks, fuzzy methods, Naïve Bayes, and regression based techniques. This paper presents a novel knowledge based system for short-term load forecasting. The algorithms of Affinity Propagation and Binary Firefly Algorithm are integrated in knowledge based system. Besides, the proposed system has minimum operational time as compared to other techniques used in the paper. Moreover, the precision of the proposed model is improved by a different priority index to select similar days. The similarity in climate and date proximity are considered all together in this index. Furthermore, the whole system is distributed in sub-systems (regions) to measure the consequences of temperature. Additionally, the predicted load of the entire system is evaluated by the combination of all predicted outcomes from all regions. The paper employs the proposed knowledge based system on real time data. The proposed scheme is compared with Deep Belief Network and Fuzzy Local Linear Model Tree in terms of accuracy and operational cost. In addition, the presented system outperforms other techniques used in the paper and also decreases the Mean Absolute Percentage Error (MAPE) on a yearly basis. Furthermore, the novel knowledge based system gives more efficient outcomes for demand load forecasting.
Nowadays, automated appliances are exponentially increasing. Therefore, there is a need for a scheme to accomplish the electricity demand of automated appliances. Recently, many Demand Side Management (DSM) schemes have been explored to alleviate Electricity Cost (EC) and Peak to Average Ratio (PAR). In this paper, energy consumption problem in a residential area is considered. To solve this problem, a heuristic based DSM technique is proposed to minimize EC and PAR with affordable user’s Waiting Time (WT). In heuristic techniques: Bacterial Foraging Optimization Algorithm (BFOA) and Flower Pollination Algorithm (FPA) are implemented.
In this paper, a hybrid optimization algorithm is proposed to solve multiobjective optimal power flow problems (MO-OPF) in a power system. The hybrid algorithm, named DA-PSO, combines the frameworks of the dragonfly algorithm (DA) and particle swarm optimization (PSO) to find the optimized solutions for the power system. The hybrid algorithm adopts the exploration and exploitation phases of the DA and PSO algorithms, respectively, and was implemented to solve the MO-OPF problem. The objective functions of the OPF were minimization of fuel cost, emissions, and transmission losses. The standard IEEE 30-bus and 57-bus systems were employed to investigate the performance of the proposed algorithm. The simulation results were compared with those in the literature to show the superiority of the proposed algorithm over several other algorithms; however, the time computation of DA-PSO is slower than DA and PSO due to the sequential computation of DA and PSO.
With the emergence of smart grid (SG), the consumers have the opportunity to integrate renewable energy sources (RESs) and take part in demand side management (DSM). In this paper, we introduce generic home energy management control system (HEMCS) model having energy management control unit (EMCU) to efficiently schedule household load and integrate RESs. The EMCU is based on genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO) and our proposed genetic WDO (GWDO) algorithm to schedule appliances of single and multiple homes. For energy pricing, real time pricing (RTP) and inclined block rate (IBR) are combined, because in case of only RTP there is a possibility of building peaks during off peak hours that may damage the entire power system. Moreover, to control demand under the capacity of electricity grid station feasible region is defined and problem is formulated using multiple knapsack (MK). Energy efficient integration of RESs in SG is a challenge due to time varying and intermittent nature of RESs. In this paper, two techniques are proposed: (i) energy storage system (ESS) that smooth out variations in renewable energy generation and (ii) trading of the surplus generation among neighboring consumers. The simulation results show that proposed scheme can avoid voltage rise problem in areas with high penetration of renewable energy and also reduce the electricity cost and peak to average ratio (PAR) of aggregated load.
The optimal power flow (OPF) problem is a non-linear and non-smooth optimization problem.
OPF problemis a complicated optimization problem, especiallywhen considering the systemconstraints.
This paper proposes a new enhanced version for the grey wolf optimization technique called
Developed Grey Wolf Optimizer (DGWO) to solve the optimal power flow (OPF) problem by an
efficient way. Although the GWO is an efficient technique, it may be prone to stagnate at local
optima for some cases due to the insufficient diversity of wolves, hence the DGWO algorithm is
proposed for improving the search capabilities of this optimizer. The DGWO is based on enhancing
the exploration process by applying a random mutation to increase the diversity of population,
while an exploitation process is enhanced by updating the position of populations in spiral path
around the best solution. An adaptive operator is employed in DGWO to find a balance between the
exploration and exploitation phases during the iterative process. The considered objective functions
are quadratic fuel cost minimization, piecewise quadratic cost minimization, and quadratic fuel cost
minimization considering the valve point effect. The DGWO is validated using the standard IEEE
30-bus test system. The obtained results showed the effectiveness and superiority of DGWO for
solving the OPF problem compared with the other well-known meta-heuristic techniques.
The moth swarm algorithm (MSA) is a new meta-heuristic optimization technique inspired by the navigational style of moths in nature. This paper represents a novel modified MSA with an arithmetic crossover (MSA-AC) with the aim of improving the search for a global optimum, the convergence speed to an optimal solution and the performance of the traditional MSA. The proposed MSA-AC method was applied in 23 standard benchmark test functions and used in six CEC 2005 composite benchmark test functions. Furthermore, in order to verify the success of the optimal solution, the MSA-AC approach was used to solve the optimal power flow problem in the two-terminal high-voltage direct current (HVDC) systems of the modified New England 39-bus and the modified WSCC 9-bus test systems. The numerical results obtained from the MSA-AC were compared to both the traditional MSA method and to various optimization algorithms presented in the literature. The outcomes obtained from the comparative results indicate the potential of the proposed approach in finding the global optimum and the convergence to an optimal solution.
Renewable energy sources (RESs) are considered as reliable and green electric power generations. Photovoltaic (PV) and wind turbine (WT) are used to provide electricity in remote areas. The optimum unit sizing of hybrid RESs components is a vital challenge in a stand-alone system. This paper presents Jaya algorithm for optimum unit sizing of a PV-WT-Battery hybrid system to fulfill the consumer's load at minimal cost. The reliability of the system is considered by a maximum allowable loss of power supply probability (LP SPmax). The results obtained from the Jaya algorithm show that the PV-WT-Battery hybrid system is the most economical and cost-effective solution for all proposed LP SPmax values as compared to PV-Battery and WT-Battery systems.
Flood is common phenomena worldwide since time immemorial. Recently the change in climatic parameters has drastically affected the pattern and magnitude of flood. India being one of the tropical country face flood and drought situations every year, therefore it needs accurate assessment and forecast of flood for proper management of natural resources. An attempt has been made through the present study which consists frequency analysis on maximum daily discharge data in Betwa river at Basoda, Mohana and Shahijina gauging stations in Madhya Pradesh state using Gumbel’s Extreme value distribution and Log Pearson Type-3 distribution for 20 years period (1993-2012). The result shows that Log Pearson Type-3 distribution is better suited for Betwa basin. The results can be used by civil engineers for deciding the dimensions of hydraulic structures such as spillways, dams, bridges etc. Floods are forecasted for the different return periods for Betwa river.
This paper introduces the multi-objective cuckoo search (MOCS) method for solving the multi-objective optimal power flow (MOOPF) which is a multi-variable, multi-constraint and nonlinear programming problem. In order to speed up convergence and enhance the quality of solution, the quasi-opposition based learning mechanism is adopted to propose the multi-objective quasi-oppositional cuckoo search (MOQOCS) algorithm in this paper. In MOCS and MOQOCS methods, the feasibility-prior domination principle (FDP) is presented to ensure the feasibility of simulation result which is based on the objective values and the sum of constraint violation values. The crowding-distance sorting is considered to enhance the diversity of Pareto-optimal solutions and obtain better distributed Pareto optimal front. IEEE 30-bus and IEEE 57-bus systems have been considered to check the performance of the proposed methods. The simulation results confirm that MOQOCS algorithm obtains superior compromise solution and produces better distributed Pareto optimal front compared to other methods.