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 photovoltaie （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.

Day-ahead electricity price forecasting using back propagation neural networks and weighted least square technique

This paper proposes the day-ahead electricity price forecasting using the artificial neural networks （ANN） and weighted least square （WLS） technique in the restructured electricity markets. Price forecasting is very important for online trading, e-commerce and power system operation. Forecasting the hourly locational marginal prices （LMP） in the electricity markets is a very important basis for the decision making in order to maximize the profits/benefits. The novel approach pro- posed in this paper for forecasting the electricity prices uses WLS technique and compares the results with the results obtained by using ANNs. To perform this price forecasting, the market knowledge is utilized to optimize the selection of input data for the electricity price forecasting tool. In this paper, price forecasting for Pennsylvania-New Jersey-Maryland （PJM） interconnec- tion is demonstrated using the ANNs and the proposed WLS technique. The data used for this price forecasting is obtained from the PJM website. The forecasting results obtained by both methods are compared, which shows the effectiveness of the proposed forecasting approach. From the simulation results, it can be observed that the accuracy of prediction has increased in both seasons using the proposed WLS technique. Another important advantage of the proposed WLS technique is that it is not an iterative method.

Review of stochastic optimization methods for smart grid

This paper presents various approaches used by researchers for handling the uncertainties involved in renewable energy sources, load demands, etc. It gives an idea about stochastic programming (SP) and discusses the formulations given by different researchers for objective functions such as cost, loss, generation expansion, and voltage/V control with various conventional and advanced methods. Besides, it gives a brief idea about SP and its applications and discusses different variants of SP such as recourse model, chance constrained programming, sample average approximation, and risk aversion. Moreover, it includes the application of these variants in various power systems. Furthermore, it also includes the general mathematical form of expression for these variants and discusses the mathematical description of the problem and modeling of the system. This review of different optimization techniques will be helpful for smart grid development including renewable energy resources (RERs).

Optimal dynamic emergency reserve activation using spinning, hydro and demand-side reserves

This paper proposes an optimal dynamic reserve activation plan after the occurrence of an emergency situation （generator/transmission line outage, load increase or both）. An optimal plan is developed to handle the emergency, using the coordinated action of fast and slow reserves, for secure operation with minimum overall cost. It considers the reserves supplied by the conventional thermal generators （spinning reserves）, hydro power units and load demands （demand-side reserves）. The optimal backing down of costly/fast reserves and bringing up of slow reserves in each sub-interval in an integrated manner is proposed. The proposed reserve activation approaches are solved using the genetic algorithm, and some of the simulation results are also compared using the Matlab optimization toolbox and the general algebraic modeling system （GAMS） software. The simulation studies are performed on the IEEE 30, 57 and 300 bus test systems. These results demonstrate the advantage of the proposed integrated/dynamic reserve activation plan over the conventional/sequential approach.