Conference Paper

Home Energy Management using Optimization Techniques

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In this paper, authors calculate the performance of single home by implementing the hybridization of two techniques, i.e. Elephant Herding Optimization (EHO) and Enhanced Differential Evolution (EDE). Appliances are categorized in three different types on the basis of their usage. For the calculation of electricity bill, Real Time Pricing (RTP) is used. The objective of this paper, is to minimize the cost and Peak to Average Ratio (PAR) and to maximize the user comfort. However, results explain that there is a trade off between user comfort and cost. Moreover, in this paper, connection between electricity cost and power consumption is verified through solution space. Results explain that proposed technique performs better in terms of PAR and user comfort and EDE performs better in terms of cost.

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Appropriately designing buildings in hot and humid climates is challenging when environmental factors and the needs of the building’s occupants are considered thoroughly. Because of the serious issue of climate change, coupled with a scarcity of conventional energy sources and a high demand for better indoor environments, advancements in the design of buildings so that they are environmentally friendly and occupant-friendly houses are indispensable. The application of new building technologies intended for tropical climates is especially needed. Outdoor environmental considerations of available on-site and off-site renewable energy sources must be taken into account when designing buildings. An evaluation of comfortable and healthy indoor environments is also important factors. The optimized utilization of available energy sources is necessary for hybrid building operation. Increasing the energy efficiency of buildings makes operating the building significantly cheaper by lowering the energy consumption which could be sourced on-site. When a building is designed to take advantage of alternative energy sources, the building is energy independent from grid-connected electricity and, thus, is not prone to power failure. Such a building can rely on different energy sources to make the building work as an energy generator. Having energy-efficient and renewable energy-supported building in hot and humid climates reduces the conventional energy consumption of the area in which these buildings are located. It also lowers the energy required from the grid line, thus minimizing grid line stress. From this, greenhouse gas emissions, which contribute significantly to climate change, can be minimized.
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In this paper, a multi-objective optimization method based on the normal boundary intersection is proposed to solve the dynamic economic dispatch with demand side management of individual residential loads and electric vehicles. The proposed approach specifically addresses consumer comfort through acceptable appliance deferral times and electric vehicle charging requirements. The multi-objectives of minimizing generation costs, emissions, and energy loss in the system are balanced in a Pareto front approach in which a fuzzy decision making method has been implemented to find the best compromise solution based on desired system operating conditions. The normal boundary intersection method is described and validated.
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Demand-side management (DSM) has emerged as an important smart grid feature that allows utility companies to maintain desirable grid loads. However, the success of DSM is contingent on active customer participation. Indeed, most existing DSM studies are based on game-theoretic models that assume customers will act rationally and will voluntarily participate in DSM. In contrast, in this paper, the impact of customers' subjective behavior on each other's DSM decisions is explicitly accounted for. In particular, a noncooperative game is formulated between grid customers in which each customer can decide on whether to participate in DSM or not. In this game, customers seek to minimize a cost function that reflects their total payment for electricity. Unlike classical game-theoretic DSM studies which assume that customers are rational in their decision-making, a novel approach is proposed, based on the framework of prospect theory (PT), to explicitly incorporate the impact of customer behavior on DSM decisions. To solve the proposed game under both conventional game theory and PT, a new algorithm based on fictitious player is proposed using which the game will reach an epsilon-mixed Nash equilibrium. Simulation results assess the impact of customer behavior on demand-side management. In particular, the overall participation level and grid load can depend significantly on the rationality level of the players and their risk aversion tendency.
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In a smart grid network, demand-side management plays a significant role in allowing consumers, incentivized by utilities, to manage their energy consumption. This can be done through shifting consumption to off-peak hours and thus reducing the peak-to-average ratio (PAR) of the electricity system. In this paper, we begin by proposing a demand-side energy consumption scheduling scheme for household appliances that considers a PAR constraint. An initial optimization problem is formulated to minimize the energy cost of the consumers through the determination of the optimal usage power and operation time of throttleable and shiftable appliances, respectively. We realize that the acceptance of consumers of these load management schemes is crucial to its success. Hence, we then introduce a multi-objective optimization problem which not only minimizes the energy cost but also minimizes the inconvenience posed to consumers. In addition to solving the proposed optimization problems in a centralized manner, two distributed algorithms for the initial and the multi-objective optimization problems are also proposed. Simulation results show that the proposed demand-side energy consumption schedule can provide an effective approach to reducing total energy costs while simultaneously considering PAR constraints and consumers' preferences.
This paper presents a computationally intelligent hybrid approach to incorporate the temporal characteristics of customer baseline load (CBL) in demand response exchange (DRX) mechanism using adaptive fuzzy inference system (FIS). The proposed hybrid approach considers the temporal characteristics of load profile using utilization factor, availability factor alongside the conventional/traditional willingness factor. The relation between load criticality and flexibility in terms of utilization and availability factors has been established and incorporated into the DR seller/customer bids in DRX through dynamic costing. Various models viz. linear, nonlinear, and exponential model etc., are developed to assess varying behavior of customer with respect to the CBL profile. In addition, a FIS is developed in this paper to account for uncertain/indistinct nature of input/information provided by the customer. To improve the performance of DRX market clearing, parameters of membership functions used in FIS are adaptively tuned using heuristic approaches. The performance of proposed hybrid model using FIS is compared with the traditional approach, fuzzy, and nonfuzzy approach without considering temporal characteristics, fuzzy, and nonfuzzy approaches with temporal characteristics only. The simulation results are presented and they demonstrate the superiority of FIS based hybrid model with CBL temporal characteristics through dynamic costing when compared to other models.
In this paper, a demand-side energy management scheme is proposed to minimize the electricity cost and the peak-to-average ratio. A particle swarm optimization strategy is integrated with a game-theoretical approach in order to solve a nonconvex problem of appliance scheduling. The unknowns of the optimization problem are modeled from the user perspective taking in consideration the real-world constraints of appliances, which impose an almost constant power consumption during predefined working time periods and within time windows constrained by the users. The performance of the proposed method has been assessed with different user time constraints. The obtained results outperform a state of the art solution based on convex programming, especially when stringent user requirements are imposed. The peak-to-average ratio is reduced more than twice respect to the convex programming technique.
Short-term load forecasting (STLF) models are very important for electric industry in the trade of energy. These models have many applications in the day-today operations of electric utility(ies) like energy generation planning, load switching, energy purchasing, infrastructure maintenance and contract evaluation. A large variety of STLF models have been developed which trade-off between forecast accuracy and convergence rate. This paper presents an accurate and fast converging STLF (AFC-STLF) model for industrial applications in a smart grid. In order to improve the forecast accuracy, modifications are devised in two popular techniques: (1) mutual information (MI) based feature selection, and (2) enhanced differential evolution (EDE) algorithm based error minimization. On the other hand, the convergence rate of the overall forecast strategy is enhanced by devising modifications in the heuristic algorithm and in the training process of the artificial neural network (ANN). Simulation results show that accuracy of the newly proposed forecast model is 99.5% with moderate execution time, i.e., we have decreased the average execution of the existing Bi-level forecast strategy by 52.38%.
In this paper, we comparatively evaluate the performance of home energy management controller which is designed on the basis of heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO) and ant colony optimization (ACO). In this regard, we introduce a generic architecture for demand side management (DSM) which integrates residential area domain with smart area domain via wide area network. In addition, problem formulation is carried via multiple knapsack problem. For energy pricing, combined model of time of use tariff and inclined block rates is used. Simulation results show that all designed models for energy management act significantly to achieve our objections and proven as a cost-effective solution to increase sustainability of smart grid. GA based energy management controller performs more efficiently than BPSO based energy management controller and ACO based energy management controller in terms of electricity bill reduction, peak to average ratio minimization and user comfort level maximization.
Manually operated appliances (MOAs) are manually operated based on users’ real-time demands and their energy consumption is uncertain to other schedulable appliances (SAs). This paper represents energy consumption scheduling of home appliances under the uncertainty of the MOAs as a robust optimization problem, as uncertainty distribution of MOAs is usually unknown and not easily estimated. Among all possible energy consumption cases of the MOAs, the robust approach takes into account the worst case to reduce electricity payment of all home appliances, based on the real-time electricity pricing scheme combined with inclining block rate. Intergeneration projection evolutionary algorithm, which is a nested heuristic algorithm with inner genetic algorithm and outer particle swarm optimization algorithm, is adopted to solve the robust optimization problem. Case studies are based on one day case, and one month case with various combinations of SAs and MOAs. Simulation results illustrate the effectiveness of the proposed approach in reduction of electricity payment compared with approach without considering the uncertainty of MOAs, and approach considering MOAs with fixed pattern.
With the rapid increase of monitoring devices and controllable facilities in the demand side of electricity networks, more solid information and communication technology (ICT) resources are required to support the development of demand side management (DSM). Different from traditional computation in power systems which customizes ICT resources for mapping applications separately, DSM especially asks for scalability and economic efficiency, because there are more and more stakeholders participating in the computation process. This paper proposes a novel cost-oriented optimization model for a cloud-based ICT infrastructure to allocate cloud computing resources in a flexible and cost-efficient way. Uncertain factors including imprecise computation load prediction and unavailability of computing instances can also be considered in the proposed model. A modified priority list algorithm is specially developed in order to efficiently solve the proposed optimization model and compared with the mature simulating annealing based algorithm. Comprehensive numerical studies are fulfilled to demonstrate the effectiveness of the proposed cost-oriented model on reducing the operation cost of cloud platform in DSM.
In this paper combined demand side management strategy for residential consumers is studied for five households in South Africa. This study is twofold; the first part proposes an energy management system that combines demand side management strategies with a view of minimizing the consumer’s cost and reducing the power consumption from the grid. Appliance scheduling with a dedicated photovoltaic and storage system under time-of-use tariff shows that customers can realize cost savings and the power demanded from the grid is reduced by optimal scheduling of power sources. In the second part of this study, a model is developed to investigate the joint influence of price and CO2 emissions. It is found that CO2 emissions could give customers an environmental motivation to shift loads during peak hours, as it would enable co-optimization of electricity consumption costs and carbon emissions reductions. It is also demonstrated that the consumer’s preferences on the cost sub-functions of energy, inconvenience and carbon emissions affects the consumption pattern. These results are important for both the consumer and the electricity suppliers, as they illustrate the optimal decisions considered in the presence of trade-offs between multiple objectives. A further study crucial to the consumer on economic analysis of PV and battery system showed that the consumer could recoup their initial investment within 5 years of their investment.
Smart pricing methods using auction mechanism allow more information exchange between users and providers, and they can meet users' energy demand at a low cost of grid operation, which contributes to the economic and environmental benefit in smart grid. However, when asked to report their energy demand, users may have an incentive to cheat in order to consume more while paying less, causing extra costs for grid operation. So it is important to ensure truthfulness among users for demand side management. In this paper, we propose an efficient pricing method that can prevent users' cheating. In the proposed model, the smart meter can record user's consumption information and communicate with the energy provider's terminal. Users' preferences and consumption patterns are modeled in form of a utility function. Based on this, we propose an enhanced AGV (Arrow-d'Aspremont-Gerard-Varet) mechanism to ensure truthfulness. In this incentive method, user's payment is related to its consumption credit. One will be punished to pay extra if there is a cheat record in its consumption history. We prove that the enhanced AGV mechanism can achieve the basic qualifications: incentive compatibility, individual rationality and budget balance. Simulation results confirm that the enhanced AGV mechanism can ensure truth-telling, and benefit both users and energy providers.
Various forms of demand side management (DSM) programs are being deployed by utility companies for load flattening amongst the residential power users. These programs are tailored to offer monetary incentives to electricity customers so that they voluntarily consume electricity in an efficient way. Thus, DSM presents households with numerous opportunities to lower their electricity bills. However, systems that combine the various DSM strategies with a view to maximizing energy management benefits have not received sufficient attention. This study therefore proposes an intelligent energy management framework that can be used to implement both energy storage and appliance scheduling schemes. By adopting appliance scheduling, customers can realize cost savings by appropriately scheduling their power consumption during the low peak hours. More savings could further be achieved through smart electricity storage. Power storage allows electricity consumers to purchase power during off-peak hours when electricity prices are low and satisfy their demands when prices are high by discharging the batteries. For optimal cost savings, the customers must constantly monitor the price fluctuations in order to determine when to switch between the utility grid and the electricity storage devices. However, with a high penetration of consumer owned storage devices, the charging of the batteries must be properly coordinated and appropriately scheduled to avoid creating new peaks. This paper therefore proposes an autonomous smart charging framework that ensures both the stability of the power grid and customer savings.
Demand Side Management in Nearly Zero Energy
  • N Javaid
  • S M Hussain
  • I Ullah
  • M A Noor
  • W Abdul
  • A Almogren
  • A Alamri
Javaid, N., Hussain, S.M., Ullah, I., Noor, M.A., Abdul, W., Almogren, A. and Alamri, A., 2017. Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations. Energies, 10(8), p.1131.
Load Shifting Technique on 24Hour Basis for a Smart-Grid to Reduce Cost and Peak Demand Using Particle Swarm Optimization
  • K S S Kumar
  • M G Naik
Kumar, K.S.S. and Naik, M.G., 2017. Load Shifting Technique on 24Hour Basis for a Smart-Grid to Reduce Cost and Peak Demand Using Particle Swarm Optimization.