Non-convex constrained economic power dispatch with prohibited operating zones and piecewise quadratic cost functions

This paper is focused on the solution of the non-convex economic power dispatch problem with piecewise quadratic cost functions and practical operation constraints of generation units. The constraints of the economic dispatch problem are power balance constraint, generation limits constraint, prohibited operating zones and transmission power losses. To solve this problem, a meta-heuristic optimization algorithm named crow search algorithm is proposed. A constraint handling technique is also implemented to satisfy the constraints effectively. For the verification of the effectiveness and the superiority of the proposed algorithm, it is tested on 6-unit, 10-unit and 15-unit test systems. The simulation results and statistical analysis show the efficiency of the proposed algorithm. Also, the results confirm the superiority and the high-quality solutions of the proposed algorithm when compared to the other reported algorithms

Solving practical economic load dispatch problem using crow search algorithm

The practical economic load dispatch problem is a non-convex, non-smooth, and non-linear optimization problem due to including practical considerations such as valve-point loading effects and multiple fuel options. An optimization algorithm named crow search algorithm is proposed in this paper to solve the practical non-convex economic load dispatch problem. Three cases with different economic load dispatch configurations are studied. The simulation results and statistical analysis show the efficiency of the proposed crow search algorithm. Also, the simulation results are compared to the other reported algorithms. The comparison of results confirm the high-quality solutions and the effectiveness of the proposed method for solving the non-convex practical economic load dispatch problem

Economic Load Dispatch problem based on Search and Rescue optimization algorithm

The Search and Rescue optimization algorithm (SAR) is a recent metaheuristic inspired by the exploration’s behaviour for humans throughout search and rescue processes. The SAR is applied to solve the Combined Emission and Economic Dispatch (CEED) and Economic Load Dispatch (ELD). The comparative performance of SAR against several metaheuristic methods was performed to assess its reliability. These algorithms include the Earthworm optimization algorithm (EWA), Grey wolf optimizer (GWO), Tunicate Swarm Algorithm (TSA) and Elephant Herding Optimization (EHO) for the same two networks study. Also, the proposed SAR method is compared with other literature algorithms such as Sine Cosine algorithm, Monarch butterfly optimization, Artificial Bee Colony, Chimp Optimization Algorithm, Moth search algorithm. The cases applied in this work are seven cases: three cases of 6-unit for ELD issue, three cases of 6-unit for CEED issue and 10-unit for ELD problem. The evaluation of counterparts is performed for 30 different runs based on measuring the Friedman rank test and robustness curves. Furthermore, the standard deviation, maximum objective function, minimum, mean and values over 30 different runs are applied for a statistical analysis of all used techniques. The obtained results proved the superiority of the SAR in determining the fitness function of ELD and CEED is minimizing the cost of fuel for ELD and emission and fuel costs for CEED

Intuitionistic fuzzy-based TOPSIS method for multi-criterion optimization problem: a novel compromise methodology

The decision-making process is characterized by some doubt or hesitation due to the existence of uncertainty among some objectives or criteria. In this sense, it is quite difficult for decision maker(s) to reach the precise/exact solutions for these objectives. In this study, a novel approach based on integrating the technique for order preference by similarity to ideal solution (TOPSIS) with the intuitionistic fuzzy set (IFS), named TOPSIS-IFS, for solving a multi-criterion optimization problem (MCOP) is proposed. In this context, the TOPSIS-IFS operates with two phases to reach the best compromise solution (BCS). First, the TOPSIS approach aims to characterize the conflicting natures among objectives by reducing these objectives into only two objectives. Second, IFS is incorporated to obtain the solution model under the concept of indeterminacy degree by defining two membership functions for each objective (i.e., satisfaction degree, dissatisfaction degree). The IFS can provide an effective framework that reflects the reality contained in any decision-making process. The proposed TOPSIS-IFS approach is validated by carrying out an illustrative example. The obtained solution by the approach is superior to those existing in the literature. Also, the TOPSIS-IFS approach has been investigated through solving the multi-objective transportation problem (MOTP) as a practical problem. Furthermore, impacts of IFS parameters are analyzed based on Taguchi method to demonstrate their effects on the BCS. Finally, this integration depicts a new philosophy in the mathematical programming field due to its interesting principles

Reliability study under the smart grid paradigm using computational intelligent techniques and renewable energy sources.

Doctoral Degree. University of KwaZulu-Natal, Durban.The increase in the demand for a reliable electricity supply by the utilities and consumers has necessitated the evaluation of the reliability of power systems. A reliable electricity supply is characterized by no or minimal duration and frequency of supply outages. Current power systems are changing due to increasing power demand and depletion of fossil fuel deposits. These changes are related to smart grids which are intelligent electric networks that are capable of using demand management methods, supporting communication devices and monitoring of consumer energy consumption. They can also integrate renewable energy sources thereby reducing reliance on fossils fuel sources. The main objective of this study is to optimize power systems operations and improve reliability. Different optimization methods are proposed in this study to address the issues of power systems operations. These optimization problems consider different constraints for maximum operations of the power systems. Case studies are used to confirm the proposed methods using the historical and climatic data for the City of Pietermaritzburg (29.37°S and 30.23°E), and Newcastle (27.71°S, 29.99°E) South Africa. Firstly, the implementation of the back-propagation algorithm method of the artificial neural networks (ANNs) for designing a predictive model for power system outage is proposed. The results obtained are found to be satisfactory. In situations where there is the problem of accessibility to large system data and presence of multiple system constraints, another method is proposed. This second technique proposes the application of a maximum entropy function-based multi-constrained event-driven outage prediction model, using the collaborative neural network (CONN) algorithm. The outcome is better than the conventional event-driven methods. Lastly, an adaptive model predictive control (AMPC) method with the integration of renewable energy sources (RESs) and a battery energy storage system (BESS) is proposed to further improve the reliability of the power system. The developed method uses a modified Roy Billinton Test System (RBTS) to implement the reliability improvement of the power system. The proposed computational intelligent techniques fulfil the necessities of operation robustness, implementation simplicity and reliability improvement of the power systems

Deep Reinforcement Learning for the Optimization of Building Energy Control and Management

Most of the current game-theoretic demand-side management methods focus primarily on the scheduling of home appliances, and the related numerical experiments are analyzed under various scenarios to achieve the corresponding Nash-equilibrium (NE) and optimal results. However, not much work is conducted for academic or commercial buildings. The methods for optimizing academic-buildings are distinct from the optimal methods for home appliances. In my study, we address a novel methodology to control the operation of heating, ventilation, and air conditioning system (HVAC). We assume that each building in our campus is equipped with smart meter and communication system which is envisioned in the future smart grid. For academic and commercial buildings, HVAC systems consume considerable electrical energy and impact the personnels in the buildings which is interpreted as monetary value in this article. Therefore, we define social cost as the combination of energy expense and cost of human working productivity reduction. We implement game theory and formulate a controlling and scheduling game for HVAC system, where the players are the building managers and their strategies are the indoor temperature settings for the corresponding building. We use the University of Denver campus power system as the demonstration smart grid and it is assumed that the utility company can adopt the real-time pricing mechanism, which is demonstrated in this paper, to reflect the energy usage and power system condition in real time. For general scenarios, the global optimal results in terms of minimizing social costs can be reached at the Nash equilibrium of the formulated objective function. The proposed distributed HVAC controlling system requires each manager set the indoor temperature to the best response strategy to optimize their overall management. The building managers will be willing to participate in the proposed game to save energy cost while maintaining the indoor in comfortable zone. With the development of Artificial Intelligence and computer technologies, reinforcement learning (RL) can be implemented in multiple realistic scenarios and help people to solve thousands of real-world problems. Reinforcement Learning, which is considered as the art of future AI, builds the bridge between agents and environments through Markov Decision Chain or Neural Network and has seldom been used in power system. The art of RL is that once the simulator for a specific environment is built, the algorithm can keep learning from the environment. Therefore, RL is capable of dealing with constantly changing simulator inputs such as power demand, the condition of power system and outdoor temperature, etc. Compared with the existing distribution power system planning mechanisms and the related game theoretical methodologies, our proposed algorithm can plan and optimize the hourly energy usage, and have the ability to corporate with even shorter time window if needed. The combination of deep neural network and reinforcement learning rockets up the research of deep reinforcement learning, and this manuscript contributes to the research of power energy management by developing and implementing the deep reinforcement learning to control the HVAC systems in distribution power system. Simulation results prove that the proposed methodology can set the indoor temperature with respect to real-time pricing and the number of inside occupants, maintain indoor comfort, reduce individual building energy cost and the overall campus electricity charges. Compared with the traditional game theoretical methodology, the RL based gaming methodology can achieve the optiaml resutls much more quicker

A survey on the development status and application prospects of knowledge graph in smart grids

With the advent of the electric power big data era, semantic interoperability and interconnection of power data have received extensive attention. Knowledge graph technology is a new method describing the complex relationships between concepts and entities in the objective world, which is widely concerned because of its robust knowledge inference ability. Especially with the proliferation of measurement devices and exponential growth of electric power data empowers, electric power knowledge graph provides new opportunities to solve the contradictions between the massive power resources and the continuously increasing demands for intelligent applications. In an attempt to fulfil the potential of knowledge graph and deal with the various challenges faced, as well as to obtain insights to achieve business applications of smart grids, this work first presents a holistic study of knowledge-driven intelligent application integration. Specifically, a detailed overview of electric power knowledge mining is provided. Then, the overview of the knowledge graph in smart grids is introduced. Moreover, the architecture of the big knowledge graph platform for smart grids and critical technologies are described. Furthermore, this paper comprehensively elaborates on the application prospects leveraged by knowledge graph oriented to smart grids, power consumer service, decision-making in dispatching, and operation and maintenance of power equipment. Finally, issues and challenges are summarised.Comment: IET Generation, Transmission & Distributio