Robust optimization based optimal chiller loading under cooling demand uncertainty

Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming

A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on Mixed Integer Genetic Algorithm

A new wind power prediction method based on ridgelet transforms, hybrid feature selection and closed-loop forecasting

Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm

In this paper short term power forecast of wind and solar power is proposed to evaluate the available output power of each production component. In this model, lead acid batteries used in proposed hybrid power system based on wind-solar power system. So, before the predicting of power output, a simple mathematical approach to simulate the lead–acid battery behaviors in stand-alone hybrid wind-solar power generation systems will be introduced. Then, the proposed forecast problem will be evaluated which is taken as constraint status through state of charge (SOC) of the batteries. The proposed forecast model includes a feature selection filter and hybrid forecast engine based on neural network (NN) and an intelligent evolutionary algorithm. This method not only could maintain the SOC of batteries in suitable range, but also could decrease the on-or-off switching number of wind turbines and PV modules. Effectiveness of the proposed method has been applied over real world engineering data. Obtained numerical analysis, demonstrate the validity of proposed method.

A new prediction model of battery and wind-solar output in hybrid power system

In this paper, a new prediction model is introduced based on hybrid forecast engine and new feature selection. In this model, the load signal is filtered by feature selection to filter out the best candidates. Then, the proposed forecast engine is predicted the output of feature selection. In this model, the weights of proposed forecast engine are optimised by an intelligent algorithm to increase its accuracy. Effectiveness of the proposed method is applied over real-world engineering test case and compared with other different well-known methods. Obtained results proof the validity of the proposed method.

A new prediction model of electricity load based on hybrid forecast engine

In the restructured power system, a large consumer can provide its required demand by using multiple options. Pool market, bilateral contracts, and self-generating units are some of the available options for power procurement. The main goal of large consumers from participation in the power market is procuring power at the minimum cost. The power price in the pool market has uncertainty, and to cope with this problem, bilateral contracts with predetermined prices can be considered by a large consumer. To model uncertainty in the pool market, different methods such as stochastic programming, robust optimization approach, and information gap decision method can be used. Renewable energy sources can be used as self-generating units to meet some part of the required demand of the large consumer. Also, energy storage systems can be implemented to store energy during off-peak demand periods and use the stored energy during peak demand periods. Different types of storage systems such as electrical storage systems, thermal storage systems, and cooling storage systems are used to store electricity, heating, and cooling energies, respectively. In this chapter, reviews of considered methods to solve the problem are presented and the obtained results are discussed.

The Concept of Large Consumer

In this paper, a new technique is suggested for best electric load forecasting of South Korea. The proposed study is based on an improved metaheuristic methodology. This paper proposes a new improved version of Support Vector Machine for Regression (SVR) base on the proposed algorithm. The algorithm is based on a modified version of Crow Search Algorithm (ACSA), where, by combining by the SVR, generates an efficient classifier. Although, different drawbacks of using original CSA are stated, the proposed ACSA method provides a proper modification for resolving of these shortcomings. The forecasting model has been then performed to a historical data from South Korea to indicate the algorithm efficiency and the results are compared with some other methods, including SVR‐CGA, SVR‐CAS, ANN, and simple Regression to show the suggested method performance. Final results show that MAPD, MSE, and RMSE values of the test data with lower values for South, North, Center, and East, provided better results based on the proposed SVR‐ACSA based on different values of center and kernel of the SVR. Experimental results on the case study shows that the minimum MAPD has been added which is happened in Northern regional is 1.1062, 1.1336, 1.1065, 1.02, 2.23, 1.96, and 1.49 for SVR‐ACSA, SVR‐CGA, SVR‐CAS, ANN, Regression, LSTM‐RNN, and ELM which indicates the method's higher efficiency.

Milad Teimourian

Papers

A new prediction model of battery and wind-solar output in hybrid power system

A new prediction model of electricity load based on hybrid forecast engine

The Concept of Large Consumer

Optimal electric load forecasting for systems by an adaptive Crow Search Algorithm: A case study