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Appliance Scheduling for Energy Management with User Preferences
Abstract
Demand Side Management (DSM) technique encourages the consumers to adjust their energy usage pattern to get optimized results for achieving the goal of minimizing the electricity consumption cost. This mechanism provides benefits to both side customer and utility in terms of cost reduction and grid stability. To regulate the increasing energy demand extensive research is being carried out for possible implementation of different DSM techniques. DSM technique ensures the user participation for achieving the energy optimization goal. User preferences and comfort is much desirable while achieving the goal of reducing Peak to average ratio (PAR), energy cost reduction and grid stability. In this paper we are proposing an Energy Management System (EMS) by considering different user environments along with the users preferences for shifting the appliances operational time. We have selected home and office environments for implementing our EMS. We are using GA, a heuristic technique, to solve the energy management problem.
... (1) Dispatching by establishing a fixed mathematical model of household electricity consumption: Literature [1] proposes multiple optimization objectives based on load peak and electricity cost minimization, and uses hybrid coding genetic algorithm to solve the problem to realize household electricity scheduling. Literature [2] aims at reducing the peak-to-average ratio (PAR), reducing energy costs and ensuring grid stability, and proposes a model solution method combining genetic algorithm and neural network to achieve a balance between user costs and grid stability. However, these fixed mathematical models of household electricity use are difficult to deal with the complexity of the scheduling environment and the randomness of electricity use behavior. ...
... The mathematical formula of the Metropolis criterion is shown in (2). ...
... (6) Calculate the updated value of Q(s, a) according to formula (10); (7) Judging whether the termination condition is met, usually the termination condition is set as the temperature reaching the minimum value or whether the current state is the final state. If the termination condition is not satisfied, select the temperature reduction coefficient according to the temperature decay function formula (12), update the temperature and reset the number of iterations; then go to step (2) to enter the next training; if it is satisfied, output the optimal strategy * π . ...
Traditional household power dispatching methods are difficult to deal with the complexity of dispatching environment and the randomness of power consumption behavior, and the QLearning algorithm is prone to fall into local optimal solutions and slow convergence when solving problems, this paper proposes a new method based on SA-α-QLearning’s home electricity scheduling strategy solution method. Firstly, a multi-intelligent Markov decision process model is established based on household electrical equipment; then the learning rate of a single value in the QLearning algorithm is replaced by a linear iterative learning rate; finally, a simulated annealing (SA) is used to optimize the QLearning algorithm to solve the model, by taking the Q value change difference as the new solution acceptance probability of Metropoils criterion and the dynamic adjustment temperature reduction coefficient, it alleviates the problem that the QLearing algorithm is easy to fall into the local optimal solution and the convergence speed is slow. Through a large number of comparative experiments, it is proved that the proposed method has a significant improvement in the solution of household electricity dispatching strategy.
... Appliances scheduling refers to providing optimised energy consumption patterns that reduce cost and mitigate peak-loads. This optimisation requires a load shift from high price periods to low price periods and from high load time to low load time during a typical day (Shaheen et al., 2016). The value creation is based on sending price signals through a smart meter, assuming a Time of Use pricing mechanism in which the prices change hourly during the day. ...
... Studies show that users make a trade-off between comfort and cost. Scheduling of appliances with Energy Management Systems (EMS) produces more efficient results by reducing the cost and peaks when the user is willing to offer more delay in shifting the appliances (Shaheen et al., 2016). ...
... The base loads are loads that must be turned on without delays, such as lighting and networking, hence cannot participate in the DR service, the elastic load are the loads that can be shifted and interrupted, such as washing machines. Inelastic loads are the loads that can be shifted but cannot be interrupted (cut) during operation, such as the HVAC and water pump (Shaheen et al., 2016). Intermittency is an attribute of the renewable energy technologies and refers to the fluctuations in the production of the renewable energy resources, thus the uncertainty of supply ( Rodrigues et al., 2016). ...
The accumulation of greenhouse gases in the atmosphere, produced by human activities in the energy sector is one of the main causes of climate change. Therefore, the decarbonization of power systems has become an urgent need to mitigate the effects of climate change and achieve the energy transition. The share of renewable energy technologies has been increasing mainly due to the participation of new market players. Today, however, one of the great challenges is to maintain the electricity system’s balance and security despite the large amount of renewable energy resources connected to the grid. One of the approaches to deal with this issue and to increase power system flexibility is the Demand Response (DR). This thesis examines this new approach and shows the interest to rethink the relations between different stakeholders, to bring out new business models in order to deploy innovations for energy transition. The implemented research methodology in this thesis consists of a systematic literature review and an investigation of empirical data of 15 European energy start-ups. As a result, the thesis provides the research community with (1) a grouping method to classify different Energy Business Models (EBMs) and an initial synthesis of the EBMs identified in the literature; (2) a framework to analyse starts ups in the energy sector, completed with the analysis of 15 energy starts ups; (3) and a conceptual tool for DR innovation, known as the Demand Response Business Model Canvas (DRBMC), which includes 12 interrelated elements. This canvas aims at evaluating DR activities and supporting the emergence of new DR business models. These results can also help entrepreneurs explore new demand response market opportunities, enabling a better understanding and providing a simplified analytic framework of existing business practices.
... Load shifting is a mechanism for enabling consumers/ prosumers to shift their load consumption to offpeak periods (Shaheen et al., 2016), or to periods with high renewable energy production (Yao et al., 2016). Scheduling the consumption of prosumers with rooftop photovoltaic systems and shifting their deferrable loads to hours with high solar power generation can lead to reductions in the energy expenses of users, and mitigate voltage rise problems in the distribution network (Yao et al., 2016). ...
Wind and solar power generation have been rapidly increasing on a global scale; this increase is limited by the capacities of the existing grids at maintaining balance between supply and demand to accommodate the fluctuations of these renewable energy resources. Therefore, grid flexibility has become a key factor in power systems. This study focuses on demand response business models (DRBMs), which have great potential for fostering energy flexibility in a cost-efficient and sustainable manner. Based on the literature review and empirical data from a case study, a business model analytical framework is proposed to explore the demand response potential based on value proposition, value creation and delivery, and value capture. This DRBM framework is characterised by nine elements: flexibility product, flexibility market segment, service attributes, demand response resources, resource availability, demand response mechanism, communication channels, cost structures, and revenue model. Based on this framework, a visualisation tool is proposed to help researchers and practitioners understand, integrate, and develop flexible electricity products. The application of this tool is then presented for electric vehicles as an example. The tool is valuable for evaluating the initial and untapped potentials of commercial demand response in electricity markets. This study thus contributes to the body of demand response literature via development of a holistic approach to assist recognition and creation of business models in emerging electricity markets.
... For example, retrofitting existing meters into smart meters to do real-time monitoring and evaluation of the grid was considered in [30]. What is, therefore, noticeable in almost all the studies reviewed is that they assume that the cost of peaking is the critical constraint on the overall network as well as the preservation of consumers' comfort of use [31], [32]. ...
While the causes of power system outages are often complex and multi-faceted, an apparent deficit in generation compared to a known demand for electricity could be more alarming. A sudden hike in demand at any given time may ultimately result in the total failure of an electricity network. In this paper, algorithms to efficiently allocate the available generation is investigated. Dynamic programming based algorithms are developed to achieve this constraint by uniquely controlling home appliances to reduce the overall demands for electricity by the consumers on the grid in context. To achieve this, heuristic optimisation method (HOM) based on the consumers’ comfort and the benefits to the electricity utility is proposed. This is then validated by simulating microload management in generation constrained power systems. Three techniques; General Shedding (GS), Priority Based Shedding (PBS) and Excess Reuse Shedding (ERS) techniques were studied for effecting efficient microload shedding. The research is aimed at reducing the burden imposed on the consumers in a generation constrained power system by the traditional load shedding approach. Additionally, the reduction of the excess curtailment is a prime objective in this paper as it helps the utility companies to reduce wastage and ultimately reduce losses resulting from over shedding. Reducing the peak-to-average ratios (PAR) on the entire network in context as a critical factor in the determination of the efficiency of an electricity network is also investigated. In the long run, the PAR affects the price charged to the final consumer. Simulation results show the associated benefits that include effectiveness, deployability, and scalability of the proposed HOM to reduce these burdens.
... Results showed that the presented approach successfully reduced the total cost and PAR. An Energy Management System (EMS) is proposed in [23] while considering user's preferences for shifting the operational time of appliances. To solve the problem of energy management, Genetic Algorithm is used. ...
Smart Gird is a technology that has brought many advantages with its evolution. Smart Grid is indispensable as it will lead us towards environmentally sustainable economic growth. Home energy management in Smart Grid is a hot research topic now a days. It aims at reducing the energy cost of users, gaining energy self-reliance and decreasing Greenhouse gas emissions. Renewable energy technologies nowadays are best suitable for off grid services without having to build extensive and complicated infrastructure. With the advent of Smart Grid (SG), the occupants have the opportunity to integrate with renewable energy sources (RESs) and to actively take part in demand side Management (DSM). This review paper is comprehensive study of various optimization techniques and their implementation with respect to electricity cost diminution, load balancing, power consumption and user's comfort maximization etc. for Home Energy Management in Smart Grid. This paper summarizes recent trends of energy usage from hybrid renewable energy integrated sources. It discusses several methodologies and techniques for hybrid renewable energy system optimization.
This paper proposes a new demand response scheduling framework for an array of households, which are grouped into different categories based on socio-economic factors, such as the number of occupants, family decomposition and employment status. Each of the households is equipped with a variety of appliances. The model takes the preferences of participating households into account and aims to minimize the overall production cost and, in parallel, to lower the individual electricity bills. In the existing literature, customers submit binary values for each time period to indicate their operational preferences. However, turning the appliances "on" or "off" does not capture the associated discomfort levels, as each appliance provides a different service and leads to a different level of satisfaction. The proposed model employs integer values to indicate household preferences and models the scheduling problem as a multi-objective mixed integer programming. The main thrust of the framework is that the multi-level preference modeling of appliances increases their "flexibility"; hence, the job scheduling can be done at a lower cost. The model is evaluated by using the real data provided by the Department of Energy & Climate Change, UK. In the computational experiments, we examine the relation between the satisfaction of consumers based on the appliance usage preferences and the electricity costs by exploring the Pareto front of the related objective functions. The results show that the proposed model leads to significant savings in electricity cost, while maintaining a good level of customer satisfaction.
For electricity consumers, there are power loads which need to be processed in a predefined time interval. The electricity price could vary between peak and off-peak time. In that case, the intelligent task scheduling module in a smart home can minimize the entire energy expense if the task control module could schedule the electrical equipments’ start times, which are determined by their power consumptions and operation time constraints. In Smart Grid environments, this Advanced Metering Infrastructure (AMI) could automatically schedule the operation time of each equipment to minimize the residential overall power consumption while satisfying the equipment’s operation constraint such as the equipment needs to be started at a time between two predefined time instants, and the power system is not overloaded at any time instant. In this research, the paper formulates the situation as an optimization problem and proposes a Genetic Algorithm (GA) based algorithm to find the optimum schedule arrangement for all the tasks in a smart home to reduce the energy cost. The performance of the GA based method is evaluated with the previous research works such as SA based method and greedy search method. The simulation results show that the GA based scheduling algorithm can efficiently and optimally minimize customers’ electricity cost.
Future smart grids will be featured by flexible supply-demand management and great penetration of renewable energy to enable efficient and economical grid operations. While the renewable energy offers a cheaper and cleaner energy supply, it introduces supply uncertainty due to the volatility of renewable source. It is therefore of practical importance to investigate the optimal exploitation of renewable energy based on the supply-demand framework of future smart grids, where the energy-providers (or the energy-users) adaptively adjust their energy-provisioning (or energy-demands) according to some system state information that takes into account the volatility of renewable energy. Specifically, we consider cost-efficient energy scheduling for residential smart grids equipped with a centralized renewable energy source. Our scheduling problem aims at: 1) quantifying the optimal utilization of renewable energy that achieves the tradeoff between the system-wide benefit from exploiting the renewable energy and the associated cost due to its volatility; and 2) evaluating how the volatility of renewable energy influences its optimal exploitation. We also propose computationally efficient and distributed algorithms to determine the optimal exploitation of renewable energy as well as the associated energy scheduling decisions.
With the development of smart grid, residents have the opportunity to schedule their power usage in the home by themselves for the purpose of reducing electricity expense and alleviating the power peak-to-average ratio (PAR). In this paper, we first introduce a general architecture of energy management system (EMS) in a home area network (HAN) based on the smart grid and then propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real-time electricity price that is transferred to an energy management controller (EMC). With the DR, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost-effective way. When only the real-time pricing (RTP) model is adopted, there is the possibility that most appliances would operate during the time with the lowest electricity price, and this may damage the entire electricity system due to the high PAR. In our research, we combine RTP with the inclining block rate (IBR) model. By adopting this combined pricing model, our proposed power scheduling method would effectively reduce both the electricity cost and PAR, thereby, strengthening the stability of the entire electricity system. Because these kinds of optimization problems are usually nonlinear, we use a genetic algorithm to solve this problem.
Demand response is a key feature of the smart grid. The addition of bidirectional communication to today's power grid can provide real-time pricing (RTP) to customers via smart meters. A growing number of appliance companies have started to design and produce smart appliances which embed intelligent control modules to implement residential demand response based on RTP. However, most of the current residential load scheduling schemes are centralized and based on either day-ahead pricing (DAP) or predicted price, which can deviate significantly from the RTP. In this paper, we propose an opportunistic scheduling scheme based on the optimal stopping rule as a real-time distributed scheduling algorithm for smart appliances' automation control. It determines the best time for appliances' operation to balance electricity bill reduction and inconvenience resulting from the operation delay. It is shown that our scheme is a distributed threshold policy when no constraint is considered. When a total power constraint exists, the proposed scheduling algorithm can be implemented in either a centralized or distributed fashion. Our scheme has low complexity and can be easily implemented. Simulation results validate proposed scheduling scheme shifts the operation to off-peak times and consequently leads to significant electricity bill saving with reasonable waiting time.
This paper deals with load control in a multiple-residence setup. The utility company adopts a cost function representing the cost of providing energy to end-users. Each residential end-user has a base load, two types of adjustable loads, and possibly a storage device. The first load type must consume a specified amount of energy over the scheduling horizon, but the consumption can be adjusted across different slots. The second type does not entail a total energy requirement, but operation away from a user-specified level results in user dissatisfaction. The research issue amounts to minimizing the electricity provider cost plus the total user dissatisfaction, subject to the individual constraints of the loads. The problem can be solved by a distributed subgradient method. The utility company and the end-users exchange information through the Advanced Metering Infrastructure (AMI)-a two-way communication network-in order to converge to the optimal amount of electricity production and the optimal power consumption schedule. The algorithm finds near-optimal schedules even when AMI messages are lost, which can happen in the presence of malfunctions or noise in the communications network. The algorithm amounts to a subgradient iteration with outdated Lagrange multipliers, for which convergence results of wide scope are established.
Demand response (DR) for residential and small commercial buildings is
estimated to account for as much as 65% of the total energy savings potential
of DR, and previous work shows that a fully automated Energy Management System
(EMS) is a necessary prerequisite to DR in these areas. In this paper, we
propose a novel EMS formulation for DR problems in these sectors. Specifically,
we formulate a fully automated EMS's rescheduling problem as a reinforcement
learning (RL) problem (referred to as the device based RL problem), and show
that this RL problem decomposes over devices under reasonable assumptions.
Compared with existing formulations, our new formulation (1) does not require
explicitly modeling the user's dissatisfaction on job rescheduling, (2) enables
the EMS to self-initiate jobs, (3) allows the user to initiate more flexible
requests and (4) has a computational complexity linear in the number of
devices. We also propose several new performance metrics for RL algorithms
applied to the device based RL problem, and demonstrate the simulation results
of applying Q-learning, one of the most popular and classical RL algorithms, to
a representative example.
A key component of the smart grid is the ability to enable dynamic residential pricing to incentivize the customer and the overall community to utilize energy more uniformly. However, the complications involved require that automated strategies be provided to the customer to achieve this goal. This paper presents a solution to the problem of optimally scheduling a set of residential appliances under day-ahead variable peak pricing in order to minimize the customer's energy bill (and also, simultaneously spread out energy usage). We map the problem to a well known problem in computer science - the multiple knapsack problem - which enables cheap and efficient solutions to the scheduling problem. Results show that this method is effective in meeting its goals.
We propose a consumption scheduling mechanism for home area load management in smart grid using integer linear programming (ILP) technique. The aim of the proposed scheduling is to minimise the peak hourly load in order to achieve an optimal (balanced) daily load schedule. The proposed mechanism is able to schedule both the optimal power and the optimal operation time for power-shiftable appliances and time-shiftable appliances respectively according to the power consumption patterns of all the individual appliances. Simulation results based on home and neighbourhood area scenarios have been presented to demonstrate the effectiveness of the proposed technique.
Wireless sensor networks (WSNs) will play a key role in the extension of the smart grid towards residential premises, and enable various demand and energy management applications. Efficient demand-supply balance and reducing electricity expenses and carbon emissions will be the immediate benefits of these applications. In this paper, we evaluate the performance of an in-home energy management (iHEM) application. The performance of iHEM is compared with an optimization-based residential energy management (OREM) scheme whose objective is to minimize the energy expenses of the consumers. We show that iHEM decreases energy expenses, reduces the contribution of the consumers to the peak load, reduces the carbon emissions of the household, and its savings are close to OREM. On the other hand, iHEM application is more flexible as it allows communication between the controller and the consumer utilizing the wireless sensor home area network (WSHAN). We evaluate the performance of iHEM under the presence of local energy generation capability, prioritized appliances, and for real-time pricing. We show that iHEM reduces the expenses of the consumers for each case. Furthermore, we show that packet delivery ratio, delay, and jitter of the WSHAN improve as the packet size of the monitoring applications, that also utilize the WSHAN, decreases.
This paper presents a multi-objective evolutionary algorithm to solve the day-ahead thermal generation scheduling problem. The objective functions considered to model the scheduling problem are: 1) minimizing the system operation cost and 2) minimizing the emission cost. In the proposed algorithm, the chromosome is formulated as a binary unit commitment matrix (UCM) which stores the generator on/off states and a real power matrix (RPM) which stores the corresponding power dispatch. Problem specific binary genetic operators act on the binary UCM and real genetic operators act on the RPM to effectively explore the large binary and real search spaces separately. Heuristics are used in the initial population by seeding the random population with two Priority list (PL) based solutions for faster convergence. Intelligent repair operator based on PL is designed to repair the solutions for load demand equality constraint violation. The ranking, selection and elitism methods are borrowed from NSGA-II. The proposed algorithm is applied to a large scale 60 generating unit power system and the simulation results are presented and compared with our earlier algorithm (26). The presented algorithm is found to outperform our earlier algorithm in terms of both convergence and spread in the final Pareto-optimal front.
General modeling techniques are presented to provide utility companies with a tool for analyzing the test data collected from their own systems. It is shown that benefits from load management may be assessed in different ways depending upon the goals to be achieved. To illustrate the use of the models presented, the results of the North and South Carolina water heater load control experiments are presented.
An approach based on dynamic programming is presented for the
dispatch of direct load control (DLC). The objective is to coordinate
DLC strategies with system unit commitment such that the system
production cost is minimized. To achieve this goal, the DLC dispatch is
first integrated into the unit commitment problem. An optimization
technique based on dynamic programming is then developed to reach the
optimal DLC dispatch strategy and system generation schedule. To
demonstrate the effectiveness of the approach, results from a sample
study performed on the Taiwan power system are described