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TACMA: total annual cost minimization algorithm for optimal sizing of hybrid energy systems

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In a stand-alone environment, a system comprising of non-renewable source, renewable energy sources (RESs), and energy storage systems like fuel cells (FCs) provide an effective and reliable solution to fulfill the user's load. In this paper, a diesel generator (DG), pho-tovoltaics (PVs), wind turbines (WTs) and FCs are modeled, optimally sized, and compared in three scenarios: PV-WT-FC-DG, PV-FC-DG, and WT-FC-DG in terms of environmental emission and total annual cost (TAC) for a home, located in Hawksbay, Pakistan. The optimal size of hybrid RESs and their components is achieved using a novel TAC minimization algorithm (TACMA). The TACMA achieves superior results in terms of TAC when it is compared to two algorithm-specific parameter-less schemes: Jaya and teaching learning-based optimization. Further, the PV-WT-FC-DG and PV-FC-DG hybrid systems are found as the most economical and nature-friendly scenarios, respectively.
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... This means that the P2G system is not restricted to supplying hydrogen to the load, but is flexible in the system operation compared to existing systems. A system operation involving the P2G system proposed by [85] is described subsequently. First, if the energy of the power system is balanced, the P2G system does not operate. ...
... An optimal energy operating system incorporating reliability and economics for each system will be considered. [4], [7], [9], [20], [21], [25], [26], [28], [32], [39], [40], [41], [47], [48], [49], [51], [55], [57], [60], [61], [76], [77], [79], [80], [110], [111], [127], [128], [129], [130], [131], [132], [133], [134], [135], [137], [138] CCHP [29], [31], [33], [34], [36], [43], [45], [52], [64], [71], [72], [75], [136], [139] P2G [2], [5], [6], [8], [9], [11], [13], [16], [17], [30], [32], [36], [38], [43], [77], [88], [89], [138], [140] TABLE V FIG [ 2,5,7,8,9,10,11,12] REFERENCES Energy conversion mode [29], [30], [31], [32], [33], [34], [36], [37], [39], [40], [43], [45], [46], [47], [48], [49], [51], [52], [55], [56], [59], [60], [61], [62], [64], [71], [72], [75], [86], [127], [128], [129], [130], [131], [132], [133], [134], [135], [136], [137], [138], [139], [140] Multi energy to Cooling [4], [10], [12], [13], [14], [15], [19], [21], [23], [25], [26], [27], [28], [29], [30], [31], [32], [36], [40], [43], [45], [62], [64], [71], [72,] [131], [134], [136], [140] Multi energy to Hydrogen [17], [14], [137] Gas boiler [8], [32], [35], [40], [42], [45], [46], [47], [51], [62], [63], [92], [93], [96], [95], [96], [97], [102], [105], [117], [123], [125] Electric heat pump [20], [26], [39], [49], [73], [79], [92], [103], [106], [123] Furnace [18], [61], [102] Electric boiler [46], [50], [73] Combustion engine [23], [63], [73] Electrolyzer [8], [10], [16], [18], [49], [50], [85], [104], Fuel cell [10], [16], [18], [49], [50], [77], [85], [104], [122] Gas turbine [18], [35], [36], [38], [42], [47], [61], [96], [97], [103], [109] Absorption chiller [23], [34], [35], [36], [42], [47], [62], [108], [109], [134] Electric chiller [23], [35], [45], [46], [63], [73], [96], [97], [105] Aircondition [40], [ 42], [47], [95], [96], [97], [103], [134] [4], [9], [10], [12], [13], [15], [17], [18], [19], [20], [23], [25], [29], [32], [33], [36], [45], [47], [48], [49], [52], [56], [60], [71], [86], [100], [128], [129], [131], [135], [136], [137], [138], [140] TES [4], [7], [10], [12], [13], [14], [18], [19], [20], [21], [23], [25], [29], [31], [33], [36], [41], [45], [47], [52], [56], [64], [71], [72], [75], [86], [91], [100], [127], [128], [129], [131], [132], [134], [135], [137], [139], [140] IES [4], [10], [13], [21], [23], [36], [41], [45], [46], [52], [63], [75] HES [2], [14], [16], [17], [30], [32], [38], [77], [137] NGS [3], [6], [14], [43], [84] ...
... An optimal energy operating system incorporating reliability and economics for each system will be considered. [4], [7], [9], [20], [21], [25], [26], [28], [32], [39], [40], [41], [47], [48], [49], [51], [55], [57], [60], [61], [76], [77], [79], [80], [110], [111], [127], [128], [129], [130], [131], [132], [133], [134], [135], [137], [138] CCHP [29], [31], [33], [34], [36], [43], [45], [52], [64], [71], [72], [75], [136], [139] P2G [2], [5], [6], [8], [9], [11], [13], [16], [17], [30], [32], [36], [38], [43], [77], [88], [89], [138], [140] TABLE V FIG [ 2,5,7,8,9,10,11,12] REFERENCES Energy conversion mode [29], [30], [31], [32], [33], [34], [36], [37], [39], [40], [43], [45], [46], [47], [48], [49], [51], [52], [55], [56], [59], [60], [61], [62], [64], [71], [72], [75], [86], [127], [128], [129], [130], [131], [132], [133], [134], [135], [136], [137], [138], [139], [140] Multi energy to Cooling [4], [10], [12], [13], [14], [15], [19], [21], [23], [25], [26], [27], [28], [29], [30], [31], [32], [36], [40], [43], [45], [62], [64], [71], [72,] [131], [134], [136], [140] Multi energy to Hydrogen [17], [14], [137] Gas boiler [8], [32], [35], [40], [42], [45], [46], [47], [51], [62], [63], [92], [93], [96], [95], [96], [97], [102], [105], [117], [123], [125] Electric heat pump [20], [26], [39], [49], [73], [79], [92], [103], [106], [123] Furnace [18], [61], [102] Electric boiler [46], [50], [73] Combustion engine [23], [63], [73] Electrolyzer [8], [10], [16], [18], [49], [50], [85], [104], Fuel cell [10], [16], [18], [49], [50], [77], [85], [104], [122] Gas turbine [18], [35], [36], [38], [42], [47], [61], [96], [97], [103], [109] Absorption chiller [23], [34], [35], [36], [42], [47], [62], [108], [109], [134] Electric chiller [23], [35], [45], [46], [63], [73], [96], [97], [105] Aircondition [40], [ 42], [47], [95], [96], [97], [103], [134] [4], [9], [10], [12], [13], [15], [17], [18], [19], [20], [23], [25], [29], [32], [33], [36], [45], [47], [48], [49], [52], [56], [60], [71], [86], [100], [128], [129], [131], [135], [136], [137], [138], [140] TES [4], [7], [10], [12], [13], [14], [18], [19], [20], [21], [23], [25], [29], [31], [33], [36], [41], [45], [47], [52], [56], [64], [71], [72], [75], [86], [91], [100], [127], [128], [129], [131], [132], [134], [135], [137], [139], [140] IES [4], [10], [13], [21], [23], [36], [41], [45], [46], [52], [63], [75] HES [2], [14], [16], [17], [30], [32], [38], [77], [137] NGS [3], [6], [14], [43], [84] ...
Article
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The increasing penetration of renewable resources causes some challenges like the electric power demand prediction uncertainty and energy surplus. Energy storage systems (ESS) are promising solutions for these challenges. However, considering the marginal capacity of ESSs according to the installation area and the economic portion of ESSs according to the installation capacity, the use of battery ESSs to reduce surplus energy is not efficient and has practical limitations. To efficiently resolve the challenges, a multi-energy system (MES) that is capable of operating different energy sources, such as natural gas storage (NGS), thermal energy storage (TES), ice energy storage (IES), and hydrogen energy storage (HES) has been proposed. The centerpiece of converting and managing multiple energy sources associated with the MES is the energy hub (EH). In this paper, we reviewed and compared the performance of existing ESSs and the MES, and the results have demonstrated the superiority of the MES. In addition, EHs that include power-to-gas, combined heat power, and combined cooling heat power, have been examined based on their structural characteristics. A review of the methods and the primary purpose of MES is also highlighted in this paper.
... [28] noted in their review article that air pollution is one of the biggest problems of megacities in China. The authors in [44], focused on environmental crimes related to carbon and other emissions in the use of energy from renewable and non-renewable energy sources. ...
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A smart city model views the city as a complex adaptive system consisting of services, resources, and citizens that learn through interaction and change in both the spatial and temporal domains. The characteristics of dynamic evolution and complexity are key issues for megacity planners and require a new systematic and modeling approach. Multiscale models involved in smart cities and megacities have recently become a popular topic because they can understand complex adaptive systems and efficiently solve complex problems at multiple scales (i.e., micro, meso, and macro) to improve system efficiency and reduce computational complexity and cost. However, there are numerous opportunities to improve this interdisciplinary field considering the lack of applicability of the multiscale modeling approach in megacities and smart cities, and the potential of multiscale modeling in various complex systems within smart cities. Therefore, a review that summarizes the state-of-art researches and opens opportunities around the theme of multiscale modeling participating in megacities and smart cities is warranted. This study, therefore, provides a comprehensive review covering the introduction of megacities, their current challenges, and their emergence in smart cities. Then, the introduction of the smart city along with its characteristics and different generations is disclosed. Moreover, we shed light on multiscale modeling, its categories (i.e., sequential multiscale modeling and concurrent multiscale modeling), and the need for multiscale modeling in megacities and smart cities along with its emerging applications. Finally, based on a literature review, the study highlights the current challenges and future directions related to multiscale modeling in megacities and smart cities, which provide a roadmap for the optimized operation of megacities and smart city systems.
... The integration of the RESs on demand side can help the consumer to minimize the electricity bill by purchasing less electricity from the electric utility. Solar energy is one of the most popular RESs and electricity generation from it is available during the daytime and it does not assist to minimize the peak load demand without installation of an energy storage system (ESS) such as batteries [7,8]. ...
Article
Full-text available
In literature, proposed approaches mostly focused on household appliances scheduling for reducing consumers' electricity bills, peak-to-average ratio, electricity usage in peak load hours, and enhancing user comfort level. The scheduling of smart home deployed energy resources recently became a critical issue on demand side due to a higher share of renewable energy sources. In this paper, a new hybrid genetic-based harmony search (HGHS) approach has been proposed for modeling the home energy management system, which contributes to minimizing consumers' electricity bills and electricity usage during peak load hours by scheduling both household appliances and smart home deployed energy resources. We have comparatively evaluated the optimization results obtained from the proposed HGHS and other approaches. The experimental results confirmed the superiority of HGHS over genetic algorithm (GA) and harmony search algorithm (HSA). The proposed HGHS scheduling approach outperformed more efficiently than HSA and GA. The electricity usage cost for completing one-day operation of household appliances was limited to 1305.7 cents, 953.65 cents, and 569.44 cents in the proposed scheduling approach for case I, case II, and case III, respectively and was observed as lower than other approaches. The electricity consumption cost was reduced upto 23.125%, 43.87% and 66.44% in case I, case II, and case III, respectively using proposed scheduling approach as compared to an unscheduled load scenario. Moreover, the electrical peak load was limited to 3.07 kW, 2.9478 kW, and 1.9 kW during the proposed HGHS scheduling approach and was reported as lower than other approaches. INDEX TERMS Demand side management, demand response program, home energy scheduling, smart grid, metaheuristic algorithm.
... According to the Global In this regard, the Independent System Operators (ISOs) are producing significant wind power and increasing their wind power generation. Efficient incorporation of wind power [76,77] benefits power systems economically because generation cost and carbon emissions tax [78]- [81] are reduced. ...
Thesis
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The revolution of power grids from traditional grids to Smart Grids (SGs) requires effective Demand Side Management (DSM) and reliable Renewable Energy Sources (RESs) incorporation in order to maintain demand, supply balance and optimize energy in an environment friendly manner. Data analytics provide solutions to the emerging challenges of power systems, such as DSM, environmental pollution (due to carbon emission), fossil fuel dependency mitigation, RESs incorporation, cost curtailment, grid’s stability and security. To efficiently manage electricity and maximize the profit of power utilities several tasks are focused in this thesis, i.e., prediction of electricity load to avoid demand and generation mismatch, wind power forecasting to satisfy energy demand effectively, electricity price forecasting for regulating market operations, carbon emissions forecasting for reducing payment of carbon tax, Electricity Theft Detection (ETD) for recovering power utilities’ revenue loss caused by electricity theft. In addition to that, a wind power forecast based DSM scheme is proposed. Furthermore, impact of RESs integration level on carbon emissions, electricity price and consumption cost is quantified. Both forecasting and classification techniques are utilized for efficient energy management. Forecasting of electricity load, price, wind power and carbon emissions is performed, whereas, classification of fair and fraudulent electricity consumers is performed. To balance electricity demand and supply, electricity load forecasting is required. Three models are proposed for this purpose, i.e., Deep Long Short-Term Memory (DLSTM), Efficient Sparse Autoencoder Nonlinear Autoregressive eXogenous network (ESAENARX) and Differential Evolution Recurrent Extreme Learning Machine (DE-RELM). DLSTM utilizes univariate data and gives single result, whereas, ESAENARX and DE-RELM model multivariate data and predict electricity load and price simultaneously. Due to adaptive and automatic feature learning mechanism, DLSTM achieves accurate results for separate forecasting of electricity load and price. ESAENARX and DE-RELM models are enhanced by newly proposed efficient feature extractor and model’s parameter tuning, respectively. Real-world datasets of ISO-NE, PJM, NYISO are used for load and price forecasting. The purpose of regulating the electricity market operations is achieved by forecasting of electricity load, price, wind power and carbon emissions. Wind power generation is predicted by an efficient model named Efficient Deep Convolution Neural Network (EDCNN). Moreover, a DSM strategy is also proposed based on predicted wind power generation. Power utilities have to pay carbon emissions tax imposed by government. To pay less carbon emissions tax, carbon emissions prediction is required, which helps in encouraging electricity consumers to shift their consumption load to low carbon price time periods of the day. For accomplishing the carbon emissions forecasting task, an efficient model named as Improved Particle Swarm Optimization based Deep Neural Network (IPSO DNN) is proposed. This model is improved by tunning the parameters of DNN by newly proposed improved optimization technique named as IPSO. ISO-NE dataset is used for wind power and carbon emissions forecasting. To reduce the financial loss of power utilities ETD is very important. For this purpose four models are proposed, named as, Differential Evolution Random Under Sampling Boosting (DE-RUSBoost), Jaya-RUSBoost, RUS Ensemble CNN (RUSE-CNN) and anomaly detection based ETD. In DE-RUSBoost and Jaya-RUSBoost, the parameters of RUSBoost classifier are tunned by DE and Jaya optimization techniques, respectively. In RUSE-CNN, RUS data balancing technique is applied along with ensemble CNN to improve ETD performance. DE-RUSBoost, Jaya-RUSBoost and RUSE-CNN are supervised model that work on labeled electricity theft data. Whereas, anomaly detection based ETD model is capable of identifying electricity theft from unlabeled electricity consumption data. Real-world datasets of SGCC, UMass, PRECON, CER, EnerNOC and LCL are used for ETD. Simulation results show that all the proposed models perform significantly better on real-world dataset as compared to their state-of-the-art counterpart models. The improved feature engineering and model hyper-parameter tuning enhance the performance of the proposed models in terms of prediction and classification results.
... Although, distributed energy resources owned by a single prosumer can be optimally operated and controlled [125], operation and control of distributed energy resources from different prosumers may raise problems in storage and generation [126]. In literature, a model based on game theory approach is proposed to coordinate each prosumer within a P2P environment where individual prosumer hopes to maximize its payoff [36,127,128,129,130,131]. ...
Thesis
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This thesis examines the privacy preserving energy management issue, taking into account both energy generation units and responsive demand in the smart grids. Firstly, because of the inherent stochastic behavior of the distributed energy resources, an optimal energy management problem is studied. Distributed energy resources are used in the decentralization of energy systems. Large penetration of distributed energy resources without the precise cybersecurity measures, such as privacy, monitoring and trustworthy communication may jeopardize the energy system and cause outages, and reliability problem for consumers. Therefore, a blockchain based decentralized energy system to accelerate electrification by improving service delivery while minimizing the cost of generation and addressing historical antipathy and cybersecurity risk is proposed. A case study of sub-Sahara Africa is considered. Also, a blockchain based energy trading system is proposed, which includes price negotiation and incentive mechanisms to address the imbalance of order. Besides, the Internet of energy makes it possible to integrate distributed energy resources and consumers. However, as the number of users involved in energy transactions increases, some factors are restricting conventional centralized energy trading. These factors include lack of trust, privacy, fixed energy pricing, and demurrage fees dispute. Therefore, additive homomorphic encryption and consortium blockchain are explored in this thesis to provide privacy and trust. Additionally, a dynamic energy pricing model is formulated based on the load demand response ratio of prosumers to address the fixed energy pricing problem. The proposed dynamic pricing model includes demurrage fees, which is a monetary penalty imposed on a prosumer if it failed to deliver energy within the agreed duration. Also, a new threat model is designed and analyzed. Secondly, mobile prosumers, such as electric vehicles offer a wide range of sophisticated services that contribute to the robustness and energy efficiency of the power grid. As the number of vehicles in the smart grid grows, it potentially exposes vehicle owners to a range of location related privacy threats. For example, when making payments, the location of vehicles is typically revealed during the charging process. Also, fixed pricing policy and lack of trust may restrict energy trading between vehicles and charging stations. Therefore, a private blockchain system is proposed to preserve the privacy of vehicle owners from linking based attack while a public blockchain system is established to enhance energy trading. Various parameters are used to formulate a demand based pricing policy for vehicles, such as time of demand, types of vehicles and locations. Using the demand based pricing policy, an optimal scheduling method is designed to maximize the vehicles both social welfare and utility. An improved consensus energy management algorithm is proposed to protect the privacy of vehicle owners by applying differential privacy. The proposed system is robust against temporal and spatial location based privacy related attacks. Thirdly, blockchain is an evolving decentralized data collection technology, which costeffectively exploits residential homes to collate large amounts of data. The problems of blockchain are the inability to withstand malicious nodes, which provide misleading information that destabilize the entire network, lack of privacy for individual node and shared data inaccuracy. Therefore, a secure system for energy users to share their multi-data using the consortium blockchain is proposed. In this system, a credibility based Byzantine fault tolerance algorithm is employed as the blockchain consensus mechanism to achieve the fault tolerance of the system. Also, a recurrent neural network is used by certain honest users with credibility to forecast the energy usage of other honest users. A recurrent neural network operates on the collated data without revealing the private information about honest users and its gradient parameters. Moreover, additive homomorphic encryption is used in the recurrent neural network to secure the collated data and the gradient parameters of the network. Also, a credibility management system is proposed to prevent malicious users from attacking the system and it consists of two layers: upper and lower. The upper layer manages global credibility that reflects the overall readiness of honest users to engage in multi-data sharing. The lower layer performs local credibility that reflects certain feedback of honest users on the accuracy of the forecast data. Lastly, combining blockchain mining and application intensive tasks increases the computational cost for resource constrained energy users. Besides, the anonymity and privacy problems of the users are not completely addressed in the existing literature. Therefore, this thesis proposes an improved sparse neural network to optimize computation offloading cost for resource constrained energy users. Furthermore, a blockchain system based on garlic routing, known as GarliChain, is proposed to solve the problems of anonymity and privacy for energy users during energy trading in the smart grid. Furthermore, a trust method is proposed to enhance the credibility of nodes in the GarliChain network. Simulations evaluate the theoretical results and prove the effectiveness of the proposed solutions. From the simulation results, the performance of the proposed model and the least-cost option varies with the relative energy generation cost of centralized, decentralized and blockchain based decentralized system infrastructure. Case studies of Burkina Faso, Cote d’Ivoire, Gambia, Liberia, Mali, and Senegal illustrate situations that are more suitable for blockchain based decentralized system. For other sub-Sahara Africa countries, the blockchain based decentralized system can cost-effectively service a large population and regions. Additionally, the proposed blockchain based levelized cost of energy reduces energy costs by approximately 95% for battery and 75% for the solar modules. The future blockchain based levelized cost of energy varies across sub-Sahara Africa on an average of about 0.049 USD/kWh as compared to 0.15 USD/kWh of an existing system in the literature. The proposed model achieves low transaction cost, the minimum execution time for block creation, the transactional data privacy of prosumers and dispute resolution of demurrage fees. Moreover, the proposed system reduces the average system overhead cost up to 66.67% as compared to 33.43% for an existing scheme. Additionally, the proposed blockchain proof of authority consensus average hash power is minimized up to 82.75% as compared to 60.34% for proof of stake and 56.89% for proof of work consensus mechanisms. Simulations are also performed to evaluate the efficacy of the proposed demand based pricing policy for mobile prosumers. From the simulation results, the proposed demand based pricing policy is efficient in terms of both low energy price and average cost, high utility and social welfare maximization as compared to existing schemes in the literature. It means that about 89.23% energy price reduction is achieved for the proposed demand based pricing policy as compared to 83.46% for multi-parameter pricing scheme, 73.86% for fixed pricing scheme and 53.07% for the time of use pricing scheme. The vehicles minimize their operating costs up to 81.46% for the proposed demand based pricing policy as compared to 80.48% for multi-parameter pricing scheme, 69.75% for fixed pricing scheme and 68.29% for the time of use pricing scheme. Also, the proposed system outperforms an existing work, known as blockchain based secure incentive scheme in terms of low energy prices and high utility. Furthermore, the proposed system achieves an average block transaction cost of 1.66 USD. Besides, after applying the differential privacy, the risk of privacy loss is minimum as compared to existing schemes. Furthermore, higher privacy protection of vehicles is attained with a lower information loss against multiple background knowledge of an attacker. To analyze the efficiency of the proposed system regarding multi-data sharing, an experimental assessment reveals that about 85% of honest users share their data with stringent privacy measures. The remaining 15% share their data without stringent privacy measures. Moreover, the proposed system operates at a low operating cost while the credibility management system is used to detect malicious users in the system. Security analysis shows that the proposed system is robust against 51% attack, transaction hacking attack, impersonation attack and the double spending attack. To evaluate the proposed system regarding energy management of resource constrained blockchain energy users, a Jaya optimization algorithm is used to accelerate the error convergence rate while reducing the number of connections between different layers of the neurons for the proposed improved sparse neural network. Furthermore, the security of the users is ensured using blockchain technology while security analysis shows that the system is robust against the Sybil attack. Moreover, the probability of a successful Sybil attack is zero as the number of attackers’ identities and computational capacities increases. Under different sizes of data to be uploaded, the proposed improved sparse neural network scheme has the least average computational cost and data transmission time as compared to deep reinforcement learning combined with genetic algorithm, and sparse evolutionary training and multi-layer perceptron schemes in the literature. Simulation results of the proposed GarliChain system show that the system remains stable as the number of path requests increases. Also, the proposed trust method is 50.56% efficient in detecting dishonest behavior of nodes in the network as compared to 49.20% of an existing fuzzy trust model. Under different sizes of the blocks, the computational cost of the forwarding nodes is minimum. Security analysis shows that the system is robust against both passive and active attacks. Malicious nodes are detected using the path selection model. Moreover, a comparative study of the proposed system with existing systems in the literature is provided.
... [28] noted in their review article that air pollution is one of the biggest problems of megacities in China. The authors in [44], focused on environmental crimes related to carbon and other emissions in the use of energy from renewable and non-renewable energy sources. ...
Preprint
Full-text available
Megacities are complex systems facing the challenges of overpopulation, poor urban design and planning, poor mobility and public transport, poor governance, climate change issues, poor sewerage and water infrastructure, waste and health issues, and unemployment. Smart cities have emerged to address these challenges by making the best use of space and resources for the benefit of citizens. A smart city model views the city as a complex adaptive system consisting of services, resources, and citizens that learn through interaction and change in both the spatial and temporal domains. The characteristics of dynamic development and complexity are key issues for city planners that require a new systematic and modeling approach. Multiscale modeling (MM) is an approach that can be used to better understand complex adaptive systems. The MM aims to solve complex problems at different scales, i.e., micro, meso, and macro, to improve system efficiency and mitigate computational complexity and cost. In this paper, we present an overview of MM in smart cities. First, this study discusses megacities, their current challenges, and their emergence to smart cities. Then, we discuss the need of MM in smart cities and its emerging applications. Finally, the study highlights current challenges and future directions related to MM in smart cities, which provide a roadmap for the optimized operation of smart city systems.
... It is essential to configure suitable sizes of batteries-based ESS for improving the quality of distributed energy resources (DERs) power and reducing the burden on utility or power grid. From users' point of view, suitable allocation of batteries-based ESS not only use RESs clean energy, but also produce certain biological and economic profits, and enhance their own behaviour [19,20]. ...
Research Proposal
Full-text available
In the electrical power grid operations and planning, the control always resides on the generation side and the power generation plants adjust their electricity generation according to the changes in electricity demand from consumers. Sometimes power generation plants produce surplus electricity, which is transmitted to the nearby area by transmission lines or stored. Therefore, it is of practical importance to balance load demand and electricity supply in the power system. In order to meet consumers’ electricity demand, demand response (DR) programs are applied to enhance the power system’s operational efficiency and minimize electricity usage during peak load hours in residential areas. On the generation side, to address optimal power flow (OPF) problems in the power system is considered as a technique for finding stable and secure operating points of electricity generation plants and their optimal scheduling on an hourly basis. In the electrical power grid operations and planning, the control always resides on the generation side and the power generation plants adjust their electricity generation according to the changes in electricity demand from consumers. Sometimes power generation plants produce surplus electricity, which is transmitted to the nearby area by transmission lines or stored. Therefore, it is of practical importance to balance load demand and electricity supply in the power system. In order to meet consumers’ electricity demand, demand response (DR) programs are applied to enhance the power system’s operational efficiency and minimize electricity usage during peak load hours in residential areas. On the generation side, to address optimal power flow (OPF) problems in the power system is considered as a technique for finding stable and secure operating points of electricity generation plants and their optimal scheduling on an hourly basis. At first, a new hybrid genetic-based harmony search (HGHS) approach has been proposed for modeling the home energy management system, which contributes to minimizing consumers’ electricity bills and electricity usage during peak load hours by scheduling both household appliances and smart home deployed energy resources. We have comparatively evaluated the optimization results obtained from the proposed HGHS and other approaches. The experimental results confirmed the superiority of HGHS over genetic algorithm (GA) and harmony search algorithm (HSA). The proposed HGHS scheduling approach outperformed more efficiently than HSA and GA. Secondly, we have proposed a bio-inspired bird swarm algorithm (BSA) to find an optimal solution to the OPF problem in the hybrid power system because it performs well in the case of optimizing the well-known Rastrigin quadratic benchmark function. Uncertainty of utility load demand and stochastic electricity output from RESs including wind and solar are incorporated into the hybrid power system for achieving accuracy in operations and planning of the system. We have used a modified IEEE-30 bus test system to verify and measure the performance of BSA and a comparison is made with well-known evolutionary metaheuristic algorithms. The proposed BSA consistently achieves more accurate and stable results than other metaheuristic algorithms. Simulation-based optimization results have shown the superiority of BSA approach to solve the OPF problems by satisfying all constraints and minimum power generation cost 863.121 $/h is achieved in case study 1. Simulation-based experiment results have indicated that by imposing the carbon tax, power generation from RESs was increased. In case study 2, the proposed BSA approach has also outperformed and minimum electricity cost 890.728 $/h is achieved as compared to other algorithms. The search efficiency of a metaheuristic algorithm based on balancing its two contradictory features – exploitation and exploration. To maintain balance in these two features of a metaheuristic algorithm is a challenging task, while handling OPF problems in a large-scale traditional power system. Finally, we have applied an auxiliary search technique based on an orthogonal experimental design method to improve the optimization performance of BSA by enhancing its exploitation search ability. The improved BSA (IBSA) is proposed to solve the OPF problem in small to large-scale traditional power systems. The OPF problem’s primary objective – minimizing power generation cost and two other objectives – reducing emission pollution and active power loss are considered for performance evaluation purposes. To verify the effectiveness, stability, and performance of the proposed IBSA, we have measured its performance on standard IEEE-30 bus, IEEE-57 bus, and IEEE-118 bus test systems. The optimization results have demonstrated that IBSA has more robustness and best convergence properties as compared to original BSA and all other algorithms. The optimal power generation cost 800.3977 $/h, 41663.5506 $/h, and 134941.0367 $/h were achieved during the proposed IBSA approach in case studies 1, 5, and 9, respectively, solving the OPF problems under primary objective. By addressing the OPF problem in a large-scale traditional power system, the minimum power loss 16.2869 MW in transmission lines was achieved from the proposed IBSA approach in case study 10.
... Prosumers self-consumed their energy and depend on the traditional system for auxiliary services. In addition, prosumers can further take benefit from the market by shifting their flexible energy load to a period of an abundant generation to minimize their energy generation costs [9]. In the case of prosumers with insufficient energy, localized energy trading with other prosumers is encouraged with an incentive. ...
Research Proposal
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In today's smart community, smart grids (SGs) have emerged as a promising solution to the future generation of the power system. In SG, smart meters automatically collect and act on information such as the behavior of consumers and suppliers. The information collected is used to improve the efficiency, reliability and sustainability of the distribution and generation of electricity. However, major challenges faced in SG are privacy, dynamic pricing and trust. This study combines pail-lier cryptosystem, differential privacy and blockchain technique to resolve the problems of data privacy, integrity and ownership. These techniques are implemented on data sharing and energy trading. Data of each prosumer is first encrypted by paillier cryptosystem at the off-chain level and then recorded in a distributed ledger at the back end level. Prosumer who want to access his encrypted data communicates with the corresponding aggregator and decrypts the encrypted data off-chain that results in minimum gas consumption and transaction fee. A new proof of authority (PoA) consensus mechanism is proposed to achieve minimum gas consumption and cost. In the PoA, the reputation score for each node is derived using the PageRank mechanism. In addition, the security analyses of PoA are performed based on similarity attack, double spending attack and birthday collision resilience. Furthermore, the characteristics of the PoA in terms of consistency, availability and partition tolerance are addressed. Note that the blockchain conducted a privacy risk negotiation with the service provider before prosumer's data is shared. In addition, blockchain serves as a broker to ensure fair energy trading among prosumers. In our scenario, two categories of prosumers are considered, such as mobile prosumers and static prosumers. This study provides three security definitions of the proposed models, which are secure two-party computation, secure temporal information and secure spatial information. In addition, threat models and their security analyses are discussed. Finally, preliminary simulation results of the proposed schemes are also presented.
Thesis
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The revolution of power grids from traditional grids to Smart Grids (SGs) requires effective Demand Side Management (DSM) and reliable Renewable Energy Sources (RESs) incorporation in order to maintain demand, supply balance and optimize energy in an environment friendly manner. Data analytics provide solutions to the emerging challenges of power systems, such as DSM, environmental pollution (due to carbon emission), fossil fuel dependency mitigation, RESs incorporation, cost curtailment, grid’s stability and security. To efficiently manage electricity and maximize the profit of power utilities several tasks are focused in this thesis, i.e., prediction of electricity load to avoid demand and generation mismatch, wind power forecasting to satisfy energy demand effectively, electricity price forecasting for regulating market operations, carbon emissions forecasting for reducing payment of carbon tax, Electricity Theft Detection (ETD) for recovering power utilities’ revenue loss caused by electricity theft. In addition to that, a wind power forecast based DSM scheme is proposed. Furthermore, impact of RESs integration level on carbon emissions, electricity price and consumption cost is quantified. Both forecasting and classification techniques are utilized for efficient energy management. Forecasting of electricity load, price, wind power and carbon emissions is performed, whereas, classification of fair and fraudulent electricity consumers is performed. To balance electricity demand and supply, electricity load forecasting is required. Three models are proposed for this purpose, i.e., Deep Long Short-Term Memory (DLSTM), Efficient Sparse Autoencoder Nonlinear Autoregressive eXogenous network (ESAENARX) and Differential Evolution Recurrent Extreme Learning Machine (DE-RELM). DLSTM utilizes univariate data and gives single result, whereas, ESAENARX and DE-RELM model multivariate data and predict electricity load and price simultaneously. Due to adaptive and automatic feature learning mechanism, DLSTM achieves accurate results for separate forecasting of electricity load and price. ESAENARX and DE-RELM models are enhanced by newly proposed efficient feature extractor and model’s parameter tuning, respectively. Real-world datasets of ISO-NE, PJM, NYISO are used for load and price forecasting. The purpose of regulating the electricity market operations is achieved by forecasting of electricity load, price, wind power and carbon emissions. Wind power generation is predicted by an efficient model named Efficient Deep Convolution Neural Network (EDCNN). Moreover, a DSM strategy is also proposed based on predicted wind power generation. Power utilities have to pay carbon emissions tax imposed by government. To pay less carbon emissions tax, carbon emissions prediction is required, which helps in encouraging electricity consumers to shift their consumption load to low carbon price time periods of the day. For accomplishing the carbon emissions forecasting task, an efficient model named as Improved Particle Swarm Optimization based Deep Neural Network (IPSO DNN) is proposed. This model is improved by tunning the parameters of DNN by newly proposed improved optimization technique named as IPSO. ISO-NE dataset is used for wind power and carbon emissions forecasting. To reduce the financial loss of power utilities ETD is very important. For this purpose four models are proposed, named as, Differential Evolution Random Under Sampling Boosting (DE-RUSBoost), Jaya-RUSBoost, RUS Ensemble CNN (RUSE-CNN) and anomly detection based ETD. In DE-RUSBoost and Jaya-RUSBoost, the parameters of RUSBoost classifier are tunned by DE and Jaya optimization techniques, respectively. In RUSE-CNN, RUS data balancing technique is applied along with ensemble CNN to improve ETD performance. DE-RUSBoost, Jaya- RUSBoost and RUSE-CNN are supervised model that work on labeled electricity theft data. Whereas, anomaly detection based ETD model is capable of identifying electricity theft from unlabeled electricity consumption data. Real-world datasets of SGCC, UMass*, PRECON, CER, EnerNOC and LCL are used for ETD. Simulation results show that all the proposed models perform significantly better on real-world dataset as compared to their state-of-the-art counterpart models. The improved feature engineering and model hyper-parameter tuning enhance the performance of the proposed models in terms of prediction and classification results.
Conference Paper
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