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Fair and reasonable allocation of regulated consumption quota ratio (RCQR) of renewable energy is the foundation and key to guarantee the effective implementation of the mechanism associated with renewable energy consumption in China. Given this background, the allocation strategy of RCQR based on game theory is proposed for guaranteeing the consumption of renewable energy in this paper. In the proposed strategy, the RCQR of renewable energy for the market entities is allocated by determining the reasonable weights based on three proportional allocation methods (i.e., electricity consumption, electricity selling profits, and electricity purchasing cost) and the group satisfaction degree method. Then, game theory is used to coordinate the inconsistency of the four methods of determining weights. Finally, an allocation case of RCQR in Anhui, China is taken as an example for demonstration to verify the effectiveness and practicability of the proposed strategy. The simulation results show that the RCQR can be appropriately allocated by the proposed strategy, and it can be obtained that there is still a certain margin between the obliged consumption under the proposed strategy of each market entity and its maximum acceptable consumption.
Difei Tang; Chenjing Dong; Xueyan Wu; Hanhan Qian; Haichao Wang; Hailong Jiang; Zhi Zhang; Yuge Chen; Xin Deng; Zhenzhi Lin; Muhammad Waseem. Allocation strategy of regulated consumption quota ratio of renewable energy based on game theory. Energy Reports 2021, 1 .
AMA StyleDifei Tang, Chenjing Dong, Xueyan Wu, Hanhan Qian, Haichao Wang, Hailong Jiang, Zhi Zhang, Yuge Chen, Xin Deng, Zhenzhi Lin, Muhammad Waseem. Allocation strategy of regulated consumption quota ratio of renewable energy based on game theory. Energy Reports. 2021; ():1.
Chicago/Turabian StyleDifei Tang; Chenjing Dong; Xueyan Wu; Hanhan Qian; Haichao Wang; Hailong Jiang; Zhi Zhang; Yuge Chen; Xin Deng; Zhenzhi Lin; Muhammad Waseem. 2021. "Allocation strategy of regulated consumption quota ratio of renewable energy based on game theory." Energy Reports , no. : 1.
With the grid's evolution, the end-users demand becomes more vital for demand side management (DSM). Accurate load forecasting (LF) is critical for power system planning and using advanced demand response (DR) strategies. To design efficient and precise LF, information about various factors that influence end-users demand is required. In this paper, the impact of different factors on electrical demand and capacity of climatic factors existence and their variation is discussed and analysed. The Pearson correlation coefficient (PCC) is utilized to express the degree of electric demand correlation with metrological and calendar factors. Then, the optimal-Bayesian regularization algorithm (BRA) based on ANN for LF is presented. The effect of the number of neurons in hidden layers on output is observed to select the most appropriate option. Additionally, heating degree days (HDDs) and cooling degree days (CDDs) indices are investigated to consider the impact of air conditioners' (ACs) loads in different seasons. Case studies on data from Dallas, Texas, USA, are used to demonstrate the influence of various factors on electrical demand. The proposed algorithm's effectiveness for LF and error formulations shows that optimal-BRA-enabled LF presents better accuracy than state-of-the-art approaches. Thus, the proposed electric demand prediction strategy could help the system operator know DR potential at different times better, leading to optimal system resources dispatching through DR actions.
Muhammad Waseem; Zhenzhi Lin; Shengyuan Liu; Zhang Jinai; Mian Rizwan; Intisar Ali Sajjad. Optimal BRA based electric demand prediction strategy considering instance‐based learning of the forecast factors. International Transactions on Electrical Energy Systems 2021, e12967 .
AMA StyleMuhammad Waseem, Zhenzhi Lin, Shengyuan Liu, Zhang Jinai, Mian Rizwan, Intisar Ali Sajjad. Optimal BRA based electric demand prediction strategy considering instance‐based learning of the forecast factors. International Transactions on Electrical Energy Systems. 2021; ():e12967.
Chicago/Turabian StyleMuhammad Waseem; Zhenzhi Lin; Shengyuan Liu; Zhang Jinai; Mian Rizwan; Intisar Ali Sajjad. 2021. "Optimal BRA based electric demand prediction strategy considering instance‐based learning of the forecast factors." International Transactions on Electrical Energy Systems , no. : e12967.
This paper proposes a novel improved polar bear optimization (IPBO) algorithm and employs it along with polar bear optimization (PBO) and chaotic population-based variants of polar bear optimization algorithm to solve combined economic emission dispatch (CEED) problem. PBO is a meta-heuristic technique inspired by the hunting mechanisms of polar bears in harsh arctic regions based only on their sense of sight. Polar bears in nature exhibits hunting of prey not only on their sight but also on their keen sense of smell. Hence, a novel improved variant of PBO which enhances its operation by equipping it with tracking capabilities utilizing polar bears sense of smell has been proposed in this study. The validity of novel IPBO is tested through 5 benchmark functions and 140 units Korean ED problem. Furthermore, the impact of different population initialization methods is also observed on the capabilities of conventional PBO. The proposed chaotic population based PBO, improved PBO (IPBO) and PBO are employed to solve IEEE 3 unit and 6-unit CEED problem. CEED is a multi-objective power system optimization problem with conflicting objectives of cost and emission. The simulations performed undertake each objective individually as well as collectively. The results achieved by each technique are analyzed statistically through Wilcoxon rank sum test (WRST), probability density function and cumulative density function. Both the statistical and numerical analysis of results showcase the strength of each solution technique as well as their ability to improve cost and emissions in the solution of CEED problem.
Saqib Fayyaz; Muhammad Kashif Sattar; Muhammad Waseem; M. Usman Ashraf; Aftab Ahmad; Hafiz Ashiq Hussain; Khalid Alsubhi. Solution of Combined Economic Emission Dispatch Problem Using Improved and Chaotic Population-Based Polar Bear Optimization Algorithm. IEEE Access 2021, 9, 56152 -56167.
AMA StyleSaqib Fayyaz, Muhammad Kashif Sattar, Muhammad Waseem, M. Usman Ashraf, Aftab Ahmad, Hafiz Ashiq Hussain, Khalid Alsubhi. Solution of Combined Economic Emission Dispatch Problem Using Improved and Chaotic Population-Based Polar Bear Optimization Algorithm. IEEE Access. 2021; 9 ():56152-56167.
Chicago/Turabian StyleSaqib Fayyaz; Muhammad Kashif Sattar; Muhammad Waseem; M. Usman Ashraf; Aftab Ahmad; Hafiz Ashiq Hussain; Khalid Alsubhi. 2021. "Solution of Combined Economic Emission Dispatch Problem Using Improved and Chaotic Population-Based Polar Bear Optimization Algorithm." IEEE Access 9, no. : 56152-56167.
As the physical carrier of the Energy Internet, integrated energy system (IES) is a future development trend in the energy field, and the optimal scheduling of IES for improving energy utilisation efficiency has become a hot topic. An optimal day‐ahead scheduling model of multiple IESs considering integrated demand response (IDR), cooperative game and virtual energy storage (VES) is proposed innovatively in this study to maximise the overall benefits of the cooperative alliance. IDR and VES are considered together for the first time to optimise the internal scheduling of each IES, where IDR can enhance the response potential on the demand side and VES can improve the scheduling flexibility of IES. Cooperative game theory is utilised to process the energy trading mechanism among multiple IESs and the Nash bargaining method is utilised to solve the cooperative game problem and obtain a fair and Pareto‐optimal energy trading strategy. The case study shows that the proposed model effectively improves the operating benefits and the renewable energy penetration levels of each IES.
Changming Chen; Xin Deng; Zhi Zhang; Shengyuan Liu; Muhammad Waseem; Yangqing Dan; Zhou Lan; Zhenzhi Lin; Li Yang; Yi Ding. Optimal day‐ahead scheduling of multiple integrated energy systems considering integrated demand response, cooperative game and virtual energy storage. IET Generation, Transmission & Distribution 2021, 1 .
AMA StyleChangming Chen, Xin Deng, Zhi Zhang, Shengyuan Liu, Muhammad Waseem, Yangqing Dan, Zhou Lan, Zhenzhi Lin, Li Yang, Yi Ding. Optimal day‐ahead scheduling of multiple integrated energy systems considering integrated demand response, cooperative game and virtual energy storage. IET Generation, Transmission & Distribution. 2021; ():1.
Chicago/Turabian StyleChangming Chen; Xin Deng; Zhi Zhang; Shengyuan Liu; Muhammad Waseem; Yangqing Dan; Zhou Lan; Zhenzhi Lin; Li Yang; Yi Ding. 2021. "Optimal day‐ahead scheduling of multiple integrated energy systems considering integrated demand response, cooperative game and virtual energy storage." IET Generation, Transmission & Distribution , no. : 1.
Background When a power system blackout occurs, it affects the economy of the country and every aspect of human life. Cascading failures can easily occur and cause a major blackout in the power grid due to the breakdown or failure of important nodes or links. Recently, transmission network reconfiguration (TNR) becomes a hot topic and has made many concerns after major blackouts of power systems. Aims TNR is the second‐stage action plan to restore power systems and plays a major role in the process of power system restoration. On the other hand, grid resilience involves a quick dynamic reconfiguration of power systems to minimize the propagation of attack influences on the grid. The motivations to include the works in this survey are based on the quality of the research performed in the transmission network reconfiguration problem for grid resilience. In this article, the state‐of‐the‐art review of recent progress in the network reconfiguration problem of the transmission system for grid resilience is discussed with practical challenges, technical issues, and power industry practices. Materials & Methods In this paper, complex network theory‐based indices with advantages, disadvantages, and their applications have been discussed to assess the important nodes and lines for network reconfiguration problem during sudden disturbances in power systems. Furthermore, optimization models have been presented with objective functions as well as their constraints. Taken together, optimization methodologies have been discussed to solve network reconfiguration problem with merits and demerits. Results This survey paper presents current trends in research and future research directions concerning transmission network reconfiguration for academic researchers and practicing engineers. Furthermore, the most current studies in improving transmission network reconfiguration problem are reviewed by highlighting their advantages and limitations. Discussion Based on a thorough comparison of literature some future perspectives are also discussed for transmission network reconfiguration problem for grid resilience. Conclusion This review paper provides a comprehensive review of current practices applied to transmission network reconfiguration. The core focus of this paper will remain on complex network theory‐based indices, optimization models, optimization methodologies, challenges, and technical issues, and discusses future direction for transmission network reconfiguration problem for grid resilience. Furthermore, the most current studies in improving transmission network reconfiguration problem are reviewed by highlighting their advantages and limitations.
Tarique Aziz; Zhenzhi Lin; Muhammad Waseem; Shengyuan Liu. Review on optimization methodologies in transmission network reconfiguration of power systems for grid resilience. International Transactions on Electrical Energy Systems 2020, 31, 1 .
AMA StyleTarique Aziz, Zhenzhi Lin, Muhammad Waseem, Shengyuan Liu. Review on optimization methodologies in transmission network reconfiguration of power systems for grid resilience. International Transactions on Electrical Energy Systems. 2020; 31 (3):1.
Chicago/Turabian StyleTarique Aziz; Zhenzhi Lin; Muhammad Waseem; Shengyuan Liu. 2020. "Review on optimization methodologies in transmission network reconfiguration of power systems for grid resilience." International Transactions on Electrical Energy Systems 31, no. 3: 1.
Conventional protection schemes in the distribution system are liable to suffer from high penetration of renewable energy source-based distributed generation (RES-DG). The characteristics of RES-DG, such as wind turbine generators (WTGs), are stochastic due to the intermittent behavior of wind dynamics (WD). It can fluctuate the fault current level, which in turn creates the overcurrent relay coordination (ORC) problem. In this paper, the effects of WD such as wind speed and direction on the short-circuit current contribution from a WTG is investigated, and a robust adaptive overcurrent relay coordination scheme is proposed by forecasting the WD. The seasonal autoregression integrated moving average (SARIMA) and artificial neuro-fuzzy inference system (ANFIS) are implemented for forecasting periodic and nonperiodic WD, respectively, and the fault current level is calculated in advance. Furthermore, the ORC problem is optimized using hybrid Harris hawks optimization and linear programming (HHO–LP) to minimize the operating times of relays. The proposed algorithm is tested on the modified IEEE-8 bus system with wind farms, and the overcurrent relay (OCR) miscoordination caused by WD is eliminated. To further prove the effectiveness of the algorithm, it is also tested in a typical wind-farm-integrated substation. Compared to conventional protection schemes, the results of the proposed scheme were found to be promising in fault isolation with a remarkable reduction in the total operation time of relays and zero miscoordination.
Mian Rizwan; Lucheng Hong; Muhammad Waseem; Shafiq Ahmad; Mohamed Sharaf; Muhammad Shafiq. A Robust Adaptive Overcurrent Relay Coordination Scheme for Wind-Farm-Integrated Power Systems Based on Forecasting the Wind Dynamics for Smart Energy Systems. Applied Sciences 2020, 10, 6318 .
AMA StyleMian Rizwan, Lucheng Hong, Muhammad Waseem, Shafiq Ahmad, Mohamed Sharaf, Muhammad Shafiq. A Robust Adaptive Overcurrent Relay Coordination Scheme for Wind-Farm-Integrated Power Systems Based on Forecasting the Wind Dynamics for Smart Energy Systems. Applied Sciences. 2020; 10 (18):6318.
Chicago/Turabian StyleMian Rizwan; Lucheng Hong; Muhammad Waseem; Shafiq Ahmad; Mohamed Sharaf; Muhammad Shafiq. 2020. "A Robust Adaptive Overcurrent Relay Coordination Scheme for Wind-Farm-Integrated Power Systems Based on Forecasting the Wind Dynamics for Smart Energy Systems." Applied Sciences 10, no. 18: 6318.
Nowadays, the attainment of flexibility in different loads for different electricity customers is an interesting topic. The assessment of flexibility is important because of the ultimate benefits to handle challenges for both electricity customers and utilities. At first, different loads that have significant contribution in energy consumption and may be potential candidates for the extraction of demand flexibility are discussed in this paper. Secondly, different descriptions of demand side flexibility and its assessment manipulation on individual and aggregate loads are explored. After investigation of different techniques some innovative definitions of flexibility indices are surveyed for numerical flexibility assessment. The information about the flexibility potential can lead to initiate different Demand Side Management (DSM) techniques that is helpful to solve electrical power systems key issues. Keeping in view this aspect, different DSM techniques for Demand Response (DR) programs are also reviewed in this paper.
Muhammad Waseem; Intisar Ali Sajjad; Shaikh Saaqib Haroon; Salman Amin; Haroon Farooq; Luigi Martirano; Roberto Napoli. Electrical Demand and its Flexibility in Different Energy Sectors. Electric Power Components and Systems 2020, 48, 1339 -1361.
AMA StyleMuhammad Waseem, Intisar Ali Sajjad, Shaikh Saaqib Haroon, Salman Amin, Haroon Farooq, Luigi Martirano, Roberto Napoli. Electrical Demand and its Flexibility in Different Energy Sectors. Electric Power Components and Systems. 2020; 48 (12-13):1339-1361.
Chicago/Turabian StyleMuhammad Waseem; Intisar Ali Sajjad; Shaikh Saaqib Haroon; Salman Amin; Haroon Farooq; Luigi Martirano; Roberto Napoli. 2020. "Electrical Demand and its Flexibility in Different Energy Sectors." Electric Power Components and Systems 48, no. 12-13: 1339-1361.
Nowadays, the most notable uncertainty for an electricity utility lies in the electrical demand and generation in power systems. Demand response (DR) accomplishment due to the home appliances energy management has acquired considerable attention for the reliable and cost-optimized power grid. The optimum schedule of home appliances is a challenging task due to uncertain electricity prices and consumption patterns. Given this background, an innovative home appliance scheduling (IHAS) framework is proposed based on the fusion of the grey wolf and crow search optimization (GWCSO) algorithm. Using the proposed technique, the cost of electricity reduction, users-comfort maximization, and peak to average ratio reduction is analyzed for home appliances in the presence of real-time price signals (RTPS). The proposed optimization algorithm is also employed for Air Conditioners (ACs) scheduling and end-users comfort maximization in its usage due to the high percentage of ACs load. Simulation results indicate that the proposed GWCSO approach is robust, computationally efficient, and outperforms conventional ones in terms of electricity cost, peak to average ratio, and it also demonstrate that there is a trade-off between users’ comfort considering appliances waiting time and electricity cost. Thus, it can provide guidance for precise electricity consumption predictions and different DR actions.
Muhammad Waseem; Zhenzhi Lin; Shengyuan Liu; Intisar Ali Sajjad; Tarique Aziz. Optimal GWCSO-based home appliances scheduling for demand response considering end-users comfort. Electric Power Systems Research 2020, 187, 106477 .
AMA StyleMuhammad Waseem, Zhenzhi Lin, Shengyuan Liu, Intisar Ali Sajjad, Tarique Aziz. Optimal GWCSO-based home appliances scheduling for demand response considering end-users comfort. Electric Power Systems Research. 2020; 187 ():106477.
Chicago/Turabian StyleMuhammad Waseem; Zhenzhi Lin; Shengyuan Liu; Intisar Ali Sajjad; Tarique Aziz. 2020. "Optimal GWCSO-based home appliances scheduling for demand response considering end-users comfort." Electric Power Systems Research 187, no. : 106477.
The transformation of a conventional power system to a smart grid has been underway over the last few decades. A smart grid provides opportunities to integrate smart homes with renewable energy resources (RERs). Moreover, it encourages the residential consumers to regulate their home energy consumption in an effective way that suits their lifestyle and it also helps to preserve the environment. Keeping in mind the techno-economic reasons for household energy management, active participation of consumers in grid operations is necessary for peak reduction, valley filling, strategic load conservation, and growth. In this context, this paper presents an efficient home energy management system (HEMS) for consumer appliance scheduling in the presence of an energy storage system and photovoltaic generation with the intention to reduce the energy consumption cost determined by the service provider. To study the benefits of a home-to-grid (H2G) energy exchange in HEMS, photovoltaic generation is stochastically modelled by considering an energy storage system. The prime consideration of this paper is to propose a hybrid optimization approach based on heuristic techniques, grey wolf optimization, and a genetic algorithm termed a hybrid grey wolf genetic algorithm to model HEMS for residential consumers with the objectives to reduce energy consumption cost and the peak-to-average ratio. The effectiveness of the proposed scheme is validated through simulations performed for a residential consumer with several domestic appliances and their scheduling preferences by considering real-time pricing and critical peak-pricing tariff signals. Results related to the reduction in the peak-to-average ratio and energy cost demonstrate that the proposed hybrid optimization technique performs well in comparison with different meta-heuristic techniques available in the literature. The findings of the proposed methodology can further be used to calculate the impact of different demand response signals on the operation and reliability of a power system.
Muhammad Muzaffar Iqbal; Malik Intisar Ali Sajjad; Salman Amin; Shaikh Saaqib Haroon; Rehan Liaqat; Muhammad Faisal Nadeem Khan; Muhammad Waseem; Muhammad Athar Shah. Optimal Scheduling of Residential Home Appliances by Considering Energy Storage and Stochastically Modelled Photovoltaics in a Grid Exchange Environment Using Hybrid Grey Wolf Genetic Algorithm Optimizer. Applied Sciences 2019, 9, 5226 .
AMA StyleMuhammad Muzaffar Iqbal, Malik Intisar Ali Sajjad, Salman Amin, Shaikh Saaqib Haroon, Rehan Liaqat, Muhammad Faisal Nadeem Khan, Muhammad Waseem, Muhammad Athar Shah. Optimal Scheduling of Residential Home Appliances by Considering Energy Storage and Stochastically Modelled Photovoltaics in a Grid Exchange Environment Using Hybrid Grey Wolf Genetic Algorithm Optimizer. Applied Sciences. 2019; 9 (23):5226.
Chicago/Turabian StyleMuhammad Muzaffar Iqbal; Malik Intisar Ali Sajjad; Salman Amin; Shaikh Saaqib Haroon; Rehan Liaqat; Muhammad Faisal Nadeem Khan; Muhammad Waseem; Muhammad Athar Shah. 2019. "Optimal Scheduling of Residential Home Appliances by Considering Energy Storage and Stochastically Modelled Photovoltaics in a Grid Exchange Environment Using Hybrid Grey Wolf Genetic Algorithm Optimizer." Applied Sciences 9, no. 23: 5226.
Air Conditioners (AC) impact in overall electricity consumption in buildings is very high. Therefore, controlling ACs power consumption is a significant factor for demand response. With the advancement in the area of demand side management techniques implementation and smart grid, precise AC load forecasting for electrical utilities and end-users is required. In this paper, big data analysis and its applications in power systems is introduced. After this, various load forecasting categories and various techniques applied for load forecasting in context of big data analysis in power systems have been explored. Then, Levenberg–Marquardt Algorithm (LMA)-based Artificial Neural Network (ANN) for residential AC short-term load forecasting is presented. This forecasting approach utilizes past hourly temperature observations and AC load as input variables for assessment. Different performance assessment indices have also been investigated. Error formulations have shown that LMA-based ANN presents better results in comparison to Scaled Conjugate Gradient (SCG) and statistical regression approach. Furthermore, information of AC load is obtainable for different time horizons like weekly, hourly, and monthly bases due to better prediction accuracy of LMA-based ANN, which is helpful for efficient demand response (DR) implementation.
Muhammad Waseem; Zhenzhi Lin; Li Yang. Data-Driven Load Forecasting of Air Conditioners for Demand Response Using Levenberg–Marquardt Algorithm-Based ANN. Big Data and Cognitive Computing 2019, 3, 36 .
AMA StyleMuhammad Waseem, Zhenzhi Lin, Li Yang. Data-Driven Load Forecasting of Air Conditioners for Demand Response Using Levenberg–Marquardt Algorithm-Based ANN. Big Data and Cognitive Computing. 2019; 3 (3):36.
Chicago/Turabian StyleMuhammad Waseem; Zhenzhi Lin; Li Yang. 2019. "Data-Driven Load Forecasting of Air Conditioners for Demand Response Using Levenberg–Marquardt Algorithm-Based ANN." Big Data and Cognitive Computing 3, no. 3: 36.