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Home energy management systems (HEMSs) based on demand response (DR) synergized with renewable energy sources (RESs) and energy storage systems (ESSs) optimal dispatch (DRSREOD) are used to implement demand-side management in homes. Such HEMSs benefit the consumer and the utility by reducing energy bills, reducing peak demands, achieving overall energy savings and enabling the sale of surplus energy. Further, a drastically rising demand of electricity has forced a number of utilities in developing countries to impose large-scale load sheddings (LSDs). A HEMS based on DRSREOD integrated with an LSD-compensating dispatchable generator (LDG) (DRSREODLDG) ensures an uninterrupted supply of power for the consumers subjected to LSD. The LDG operation to compensate the interrupted supply of power during the LSD hours; however, accompanies the release of GHGs emissions as well that need to be minimized to conserve the environment. A 3-step simulation based posteriori method is proposed to develop a scheme for eco-efficient operation of DRSREODLDG-based HEMS. The method provides the tradeoffs between the net cost of energy (CEnet) to be paid by the consumer, the time-based discomfort (T BD) due to shifting of home appliances (HAs) to participate in the HEMS operation and minimal emissions (T EM iss) from the local LDG. The search has been driven through multi-objective genetic algorithm and Pareto based optimization. The surface fit is developed using polynomial models for regression based on the least sum of squared errors and selected solutions are classified for critical tradeoff analysis to enable the consumer by choosing the best option and consulting a diverse set of eco-efficient tradeoffs between CEnet, T BD and T EM iss.
A detailed study of the potential impact of low voltage (LV) residential demand-side management (DSM) on the cost and greenhouse gas (GHG) emissions is presented. The proposed optimisation algorithm is used to shift non-critical residential loads, with the wet load category used as a case study, in order to minimise the total daily cost and emissions due to generation. This study shows that it is possible to reshape the total power demand and reduce the corresponding cost and emissions to some extent. It is also shown that, when the baseload generating mix is dominated by coal-fired generation, the daily profiles of GHG emissions and cost conflict, such that further optimisation of the cost leads to an increase in emissions.
In this work, we propose a Realistic Scheduling Mechanism (RSM) to reduce user frustration and enhance appliance utility by classifying appliances with respective constraints and their time of use effectively. Algorithms are proposed regarding functioning of home appliances. A 24 hour time slot is divided into four logical sub-time slots, each composed of 360 min or 6 h. In these sub-time slots, only desired appliances (with respect to appliance classification) are scheduled to raise appliance utility, restricting power consumption by a dynamically modelled power usage limiter that does not only take the electricity consumer into account but also the electricity supplier. Once appliance, time and power usage limiter modelling is done, we use a nature-inspired heuristic algorithm, Binary Particle Swarm Optimization (BPSO), optimally to form schedules with given constraints representing each sub-time slot. These schedules tend to achieve an equilibrium amongst appliance utility and cost effectiveness. For validation of the proposed RSM, we provide a comparative analysis amongst unscheduled electrical load usage, scheduled directly by BPSO and RSM, reflecting user comfort, which is based upon cost effectiveness and appliance utility.
Energy sustainability of hybrid energy systems is essentially a multiobjective, multiconstraint problem, where the energy system requires the capability to make rapid and robust decisions regarding the dispatch of electrical power produced by generation assets. This process of control for energy system components is known as energy management. In this paper, the application of particle swarm optimization (PSO), which is a biologically inspired direct search method, to find real-time optimal energy management solutions for a stand-alone hybrid wind-microturbine (MT) energy system, is presented. Results demonstrate that the proposed PSO-based energy management algorithm can solve an extensive solution space while incorporating many objectives such as: minimizing the cost of generated electricity, maximizing MT operational efficiency, and reducing environmental emissions. Actual wind and end-use load data were used for simulation studies and the well-established sequential quadratic programming optimization technique was used to validate the results obtained from PSO. Promising simulation results indicate the suitability of PSO for real-time energy management of hybrid energy systems.
Over the past few years, active research on algorithm development for the optimal operations of HEMSs has been performed. The objective is to compute optimized schedules for shiftable home appliances (SHAs). This is based on the demand response (DR) synergized with renewable energy sources (RESs) and ESS optimal dispatch (DRSREOD). An improved algorithm for a DRSREOD-based HEMS is proposed in this paper. This heuristic-based algorithm considers DR, photovoltaic (PV) availability, the state of charge and charge/discharge rates of the storage battery (SB) and the sharing-based parallel operation of more than one power source to supply the required load. The HEMS problem has been solved to minimize the cost of energy (CE) and time-based discomfort (TBD) with conflicting tradeoffs. The mixed scheduling (MS) of appliances (delayed scheduling (DS) for some appliances and advanced scheduling (AS) for others) is introduced to improve the CE and TBD performance parameters. An inclining block rate (IBR) scheme is also incorporated to reduce the peak load. A set of optimized tradeoffs between CE and TBD has been computed to address multi-objectivity using a multi-objective GA (MOGA) with Pareto optimization (PO) to perform the tradeoff analysis and to enable consumers to select the most feasible solution. Due to the rapid increase in demand for electricity, developing countries are facing large-scale load shedding (LS). An innovative algorithm is also proposed for the optimal sizing of a dispatchable generator (DG) that can supply a DRSREOD-based HEMS during LS hours to ensure an uninterrupted supply of power. The proposed MOGA/PO-based algorithm enables consumers to select a DG of the optimal size from among a number of optimal choices based on tradeoffs between the DG size, CE and TBD.
The home power management system is proposed with the objective to reduce the electricity cost and also to avoid the problem of high peak demand. Recently more methods have been discussed in the area of Home energy management, but prioritizing the operation of power units from customer point of view has its own benefits depending on the comfort level. The Proposed home consists of a smart electrical appliance, photovoltaic system with battery, smart communication network and a robust controller. This controller schedules the power units in response to electricity price at the Time of Use (ToU). The available power units comprising of solar power, battery power, grid supply and the utilization of home appliance are categorized and monitored regularly. The Primary power units, preferably solar are chosen automatically as per the priority of the customer. When the primary power unit (solar) is not able to supply power, due to its intermittent nature of generation, the controller shifts to next power units accordingly. The simulation results show that the proposed system based on Home Energy Management (HEM) algorithm reduces the electricity cost, peak demand problem and enhances the efficiency of energy use. The Time of Use (ToU) is considered for reducing the peak demand. The smart controller is operated based on HEM algorithm and selects the power units accordingly. Also, there is a necessity of Energy conversion from DC (solar) to AC (grid/appliance), there is feasibility of power quality disturbance. This quality of power is improved by using Selective Harmonic elimination (SHE) method. The proposed system is developed and simulated in MATLAB/SimPowerSystem (SPS).
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.
Internet of Things (IoT) has recently emerged as an enabling technology for context-aware and interconnected 'smart things.' Those smart things along with advanced power engineering and wireless communication technologies have realized the possibility of next generation electrical grid, smart grid, which allows users to deploy smart meters, monitoring their electric condition in real time. At the same time, increased environmental consciousness is driving electric companies to replace traditional generators with renewable energy sources which are already productive in user's homes. One of the most incentive ways is for electric companies to institute electricity buying-back schemes to encourage end users to generate more renewable energy. Different from the previous works, we consider renewable energy buying-back schemes with dynamic pricing to achieve the goal of energy efficiency for smart grids. We formulate the dynamic pricing problem as a convex optimization dual problem and propose a day-ahead time-dependent pricing scheme in a distributed manner which provides increased user privacy. The proposed framework seeks to achieve maximum benefits for both users and electric companies. To our best knowledge, this is one of the first attempts to tackle the time-dependent problem for smart grids with consideration of environmental benefits of renewable energy. Numerical results show that our proposed framework can significantly reduce peak time loading and efficiently balance system energy distribution.
Though the locational marginal price (LMP) may change dramatically within a single day in competitive wholesale electricity markets, most end-users are charged monthly electricity bills over flat rates. Without financial incentives, the customers are lacking of motivation to respond to the price signals, which may result in inefficient energy consumption. In Texas, Senate Bill 1125 encourages qualified residential and commercial customer classes to participate in demand response programs. This paper proposes an idea to aggregate a number of residential customers to participate in residential demand response program by employing smart appliances and a home area network to shift the coincidental peak load to off-peak hours to reap financial benefits. The operation strategies for the most representative residential load types are discussed. To further reduce electricity purchase and cut electricity bills, a solar farm with energy storage system is proposed and the control algorithm is designed accordingly. The operation strategies are simulated for a whole year and the annual costs are calculated and compared in this study. The results show that by doing load control and utilizing renewable resources, the total cost can be reduced significantly.
This paper proposes a novel optimization based home load control (HLC) to manage the operation periods of responsive electrical appliances, determine several recommended operation periods for nonresponsive appliances, and schedule the charge/discharge cycling of plug-in hybrid electric vehicle (PHEV) considering various customer preferences. The customer preferences are in the format of payment cost, interruption cost, and different operational constraints. The projected algorithm is online, in which household appliances are initially scheduled based on the payment cost, and when the home load is interrupted, the scheduling will be updated to minimize customer interruption cost. Due to vehicle to home capability of PHEV, the home outage can be managed through solving the proposed optimization problem. Several realistic case studies are carried out to examine the performance of the suggested method. In addition, the impacts of common electricity tariffs on the HLC results are investigated. The results reveal that employing the proposed HLC program benefits not only the customers by reducing their payment and interruption costs, but also utility companies by decreasing the peak load of the aggregate load demand.
Immense growth has happened in the field of microgrid (MG) and the energy management system (EMS) methods in the past decade. It is estimated that there is still a huge potential of growth remaining in the field of EMS in the coming years. The main role of EMS is to autonomously determine hour-by-hour the optimum dispatch of MG and main grid energy to satisfy load demand needs. This paper is focused on developing an advanced EMS model able to determine the optimal operating strategies regarding to energy costs minimization, pollutant emissions reduction, MG system constraints and better utilization of renewable resources of energy such as wind and photovoltaic through daily load demand. The proposed optimization model of EMS is formulated and solved based on genetic algorithm (GA). The efficient performance of the algorithm and its behavior is illustrated and analyzed in detail considering winter load demand profile.
With the decreasing of the fossil energy source and the increasing of load demand, making full use of clean and renewable energy, Distributed Generation (DG) technologies gain more and more attentions. Microgrid (MG) integrates the advantages of power generation from new energy and renewable energy power generation systems connected to the grid. MG can not only enhance the comprehensively cascaded utilization of energy, but also can be used as an effective complementary network of the utility in order to improve the power supply reliability and power quality. Based on the analysis of the structure of Microgrid, an optimization model of economic dispatch Microgrid system is established. By integrating these problems, such as the scheduling of generators, intelligent management of energy storage units and optimization of operated efficiency of the network, into a uniform optimization problem, the complexity of application optimization algorithm is reduced. In this plan, use the Isolation Niche Immune Genetic Algorithm (INIGA) to confirm the accuracy and validity of the mathematic model through some actual examples, and then used this method to compare with some other optimization approaches that usually be used to solve the Energy Management and Optimization Operation problem to show the superiority and usability of the approach mentioned here.
The advancement of renewable energy technologies has seen the emergence of customer owned grid tied wind and solar microgrids. These microgrids offer an opportunity to energy users to lower their energy costs as well as enabling the power suppliers to regulate the utility grid. However, the integration of the renewable energy based sources into the smart grid increases the complexity of the main grid. The success of this scheme will be heavily reliant on accurate real-time information exchange between the microgrid, the main grid, and the consumers. The communication between these agents will be critical in implementation of intelligent decisions by the smart grid. The microgrids will be required to relay energy forecasts information to the utility grid. Similarly, customers will be expected to submit energy demand schedules, to actively monitor energy price signals, to participate in energy bids, and to respond to energy management signals in real time. This kind of grid-user interaction will be overwhelming and could result in consumer apathy. There is therefore a need to develop smart systems that will autonomously execute all these tasks without the prompting of the customers. This paper presents one such approach. In this study, we proposed a demand side energy management for a grid connected household with a locally generated photovoltaic energy. To ensure efficient household energy management, smart scheduling of electrical appliances has also been presented.
This paper presents mathematical optimization models of residential energy hubs which can be readily incorporated into automated decision making technologies in smart grids, and can be solved efficiently in a real-time frame to optimally control all major residential energy loads, storage and production components while properly considering the customer preferences and comfort level. Novel mathematical models for major household demand, i.e., fridge, freezer, dishwasher, washer and dryer, stove, water heater, hot tub, and pool pumps are formulated. Also, mathematical models of other components of a residential energy system including lighting, heating, and air-conditioning are developed, and generic models for solar PV panels and energy storage/generation devices are proposed. The developed mathematical models result in Mixed Integer Linear Programming (MILP) optimization problems with the objective functions of minimizing energy consumption, total cost of electricity and gas, emissions, peak load, and/or any combination of these objectives, while considering end-user preferences. Several realistic case studies are carried out to examine the performance of the mathematical model, and experimental tests are carried out to find practical procedures to determine the parameters of the model. The application of the proposed model to a real household in Ontario, Canada is presented for various objective functions. The simulation results show that savings of up to 20% on energy costs and 50% on peak demand can be achieved, while maintaining the household owner's desired comfort levels.
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