• Energy Research

Abstract Providing efficient support mechanisms for renewable energy promotion has drawn much attention from researchers in the recent years. The connection of a new renewable power plant to the transmission system has impacts on different electricity market indices since the other strategic generation units change their behaviour in the new multi-agent environment. In this paper, as the main contribution to the previous literature, a combination of multi-criteria decision-making approach and multi-agent modelling technique is developed to obtain the maximum possible profits for an intended renewable generation plan and also direct the investment to be located in a way to improve electricity market indices besides supporting renewable energy promotion. Fuzzy Q-learning electricity market modelling approach in combination with the technique for order preference by similarity (TOPSIS) is used as a new decision support system for promotion of renewable energy for the first time in the literature. The proposed interactive multi-criteria decision-making framework between the independent system operator (ISO) and the renewable power plant planner provides a win-win situation that improve market indices while help the renewable power plant planning. The effectiveness of the proposed method is examined on the IEEE 30-bus test system and the results are discussed.

Power system operation in the era of post-restructuring faces several challenges: transmission congestion frequently occurs, security is deterred more than in the past, emission reduction is becoming a matter of importance and intermittent renewable power generation resources (RPGR) have been widely promoted. This paper intends to solve these challenges in a multi-objective optimisation framework. The proposed procedure comprises two stages: in the a priori stage, transmission congestion management cost (TCMC) and emission are traded-off via a proposed stochastic augmented e-constraint technique which yields a set of non-dominated solutions. In the a posteriori stage, a solution is selected by considering power system security. For this purpose, two strategies are proposed: in the first strategy, based on a proposed managerial vision, a combination of data envelopment analysis introduced by Charnes, Cooper, and Rhodes (CCR-DEA), cross-efficiency technique and robustness analysis is deployed to select the most robust super-efficient solution. The advantage of the proposed a posteriori approach is that selecting the final solution is not subjected to assigning weights to the objective functions and/or providing higher-level information. In the second strategy, first the effective scenarios due to outage of transmission components are identified using CCR-DEA and next, each scenarios’ degree of severity (DOS) is obtained using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The sums of the DOS of non-dominated solutions’ effective scenarios are evaluated for final decision making. The proposed approach is applied to IEEE 24 bus test system and the results are analysed.

AbstractThe integration of energy efficiency programs and renewable resources in energy communities (EC) incorporating smart microgrids might be the future development of sustainable cities. Cooperation among EC with high penetration of renewable energy resources will support energy procurement. Mutual energy efficiency programs in EC associated with deploying renewable resources can guarantee sustainable development significantly. This paper proposes a new sustainability index (SI) influenced by the loss of load reduction while increasing the penetration of renewable energies. A multi‐objective optimization method is used to determine the optimal size and location of renewable resources incorporating energy efficiency programs. The objective functions, that is, the total cost and SI, are based upon social, economic, environmental, and technical (SEET) considerations. Since the outputs of renewable energies are uncertain, it may increase the loss of load probability. Therefore, this paper introduces a model that can handle the uncertainties of renewable energies. A new method so‐called adaptive multi‐objective crow search algorithm (AMCSA) is developed for the trade‐off between cost and sustainability in the optimization problem. The proposed methodology cannot only minimize the total cost in EC but also will maximize the SI. Simulation studies and results analysis indicates the effectiveness of the proposed methodology for the development of sustainability and renewable energies in the smart grid.

In this paper a new approach for transmission pricing is presented. The contribution of a contract on power flow of a transmission line is used as extent-of-use criterion for transmission pricing. In order to determine the contribution of each contract on power flow of each transmission line, first the contribution of each contract on each voltage angle is determined, which is called voltage angle decomposition. To this end, DC power flow is used to compute a primary solution for voltage angle decomposition. To consider the impacts of system non-linearity on voltage angle decomposition, a method is presented to determine the share of different terms of sine argument in sine value. Then the primary solution is corrected in different iterations of decoupled Newton–Raphson power flow using the presented sharing method. The presented approach is applied to a 4-bus test system and IEEE 30-bus test system and the results are analyzed.

Exposure to extreme weather conditions increases power systems’ vulnerability in front of high impact, low probability contingency occurrence. In the post-restructuring years, due to the increasing demand for energy, competition between electricity market players and increasing penetration of renewable resources, the provision of effective resiliency-based approaches has received more attention. In this paper, as the major contribution to current literature, a novel approach is proposed for resiliency improvement in a way that enables power system planners to manage several resilience metrics efficiently in a bi-objective optimization planning model simultaneously. For demonstration purposes, the proposed method is applied for optimal placement of the thyristor controlled series compensator (TCSC). Improvement of all considered resilience metrics regardless of their amount in a multi-criteria decision-making framework is novel in comparison to the other previous TCSC placement approaches. Without loss of generality, the developed resiliency improvement approach is applicable in any power system planning and operation problem. The simulation results on IEEE 30-bus and 118-bus test systems confirm the practicality and effectiveness of the developed approach. Simulation results show that by considering resilience metrics, the performance index, importance of curtailed consumers, congestion management cost, number of curtailed consumers, and amount of load loss are improved by 0.63%, 43.52%, 65.19%, 85.93%, and 85.94%, respectively.

Maximum power point tracking (MPPT) through an effective control strategy increases the efficiency of solar panels under rapidly changing atmospheric conditions. Due to the nonlinearity of the I–V characteristics of the PV module, the Sliding Mode Controller (SMC) is considered one of the commonly used control approaches for MPPT in the literature. This paper proposed a Backstepping SMC (BSMC) method that ensures system stability using Lyapunov criteria. A fuzzy inference system replaces the saturation function, and a modified SMC is used for MPPT to ensure smooth behavior. The proposed Fuzzy BSMC (FBSMC) parameters are optimized using a Particle Swarm Optimization (PSO) approach. The proposed controller is tested through various case studies on account of MPP’s dependence on temperature and solar radiation. The controller performance is assessed in partial shading conditions as well. The simulation results show that less settling time, a small error, and enhanced power extraction capability are achieved by applying the PSO-based FBSMC approach compared to the conventional BSMC- and ABC-based PI control presented in previous research in different scenarios. Moreover, the proposed approach provides faster adaptation to temperature and solar radiation variation, ensuring faster convergence to the MPP. Finally, the robustness of the proposed controller is validated by providing variation within the system components. The result of the proposed controller clearly indicates the lowest value of RMSE measured between PV voltage and the reference voltage, as well as the RMSE between PV power and maximum power. The results also show that the proposed MPPT controller exhibits the highest dynamic efficiency and mean power.

Under competitive electricity markets, various long-term and short-term contracts based on spot price are implemented by independent market operator (IMO). An accurate forecasting technique for spot price facilitates the market participants to develop bidding strategies in order to maximize their benefit. Neural-Wavelet is a powerful method for forecasting problems under the condition of nonlinearity as well as uncertainty. In this paper, a new methodology based upon radial basis function (RBF) network is proposed to the forecasting spot price problem. To train the network, in order to apply historical information of the price behavior, some other effective parameters are used. Load level, fuel price, generation and transmission location as well as conditions are the effective parameters which are associated with general well known parameters. All these parameters are applied for learning process to an assumed neural wavelet network (NWN). Simulation results are presented in details in this paper, where these results indicate the effectiveness of the proposed forecasting tool as an accurate technique.