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A Distributed Algorithm of Appliance Scheduling for Home Energy Management System
Abstract
Demand side management encourages the users in a smart grid to shift their electricity consumption in response to varying electricity prices. In this paper, we propose a distributed framework for the demand response based on cost minimization. Each user in the system will find an optimal start time and operating mode for the appliances in response to the varying electricity prices. We model the cost function for each user and the constraints for the appliances. We then propose an approximate greedy iterative algorithm that can be employed by each user to schedule appliances. In the proposed algorithm, each user requires only the knowledge of the price of the electricity, which depends on the aggregated load of other users, instead of the load profiles of individual users. In order for the users to coordinate with each other, we introduce a penalty term in the cost function, which penalizes large changes in the scheduling between successive iterations. Numerical simulations show that our optimization method will result in lower cost for the consumers, lower generation costs for the utility companies, lower peak load, and lower load fluctuations.
... Regarding algorithmic strategies for adaptive energy consumption in HEMS, existing research works have mainly focused on PAR reduction (by reshaping the demand profile) [34], user utility maximization [35], consumption cost minimization [36], and incorporation of renewable energy [37]. Energy management problems are modeled through optimization techniques such as ILP [38], or other approaches, such as game theory [34]. ...
... Due to the complexity of optimal algorithmic strategies in HEMS, some researchers focus exclusively on heuristic approaches. In [36], for instance, Chavali et al. propose an approximate greedy algorithm, in which each EC schedules the consumption of appliances in response to varying electricity prices. The optimization model in [36] is based on minimizing cost functions for each EC. ...
... In [36], for instance, Chavali et al. propose an approximate greedy algorithm, in which each EC schedules the consumption of appliances in response to varying electricity prices. The optimization model in [36] is based on minimizing cost functions for each EC. These functions consider the constraints of the appliances and user preferences in the starting consumption time. ...
The ever-growing energy demand and the CO2 emissions caused by energy production and consumption have become critical concerns worldwide and drive new energy management and consumption schemes. In this regard, energy systems that promote green energy, customer-side participation enabled by the Internet of Things (IoT) technologies, and adaptive consumption mechanisms implemented on advanced communications technologies such as the Network Function Virtualization (NFV) emerge as sustainable and de-carbonized alternatives. On these modern schemes, diverse management algorithmic solutions can be deployed to promote the interaction between generation and consumption sides and optimize the use of available energy either from renewable or non-renewable sources. However, existing literature shows that management solutions considering features such as the dynamic nature of renewable energy generation, prioritization in energy provisioning if needed, and time-shifting capabilities to adapt the workloads to energy availability present a complexity NP-Hard. This condition imposes limits on applicability to a small number of energy demands or time-shifting values. Therefore, faster and less complex adaptive energy management approaches are needed. To meet these requirements, this paper proposes three heuristic strategies: a greedy strategy (GreedyTs), a genetic-algorithm-based solution (GATs), and a dynamic programming approach (DPTs) that, when deployed at the NFV domain, seeks the best possible scheduling of demands that lead to efficient energy utilization. The performance of the algorithmic strategies is validated through extensive simulations in several scenarios, demonstrating improvements in energy consumption and processing of demands. Additionally, simulation results reveal that the heuristic approaches produce high-quality solutions close to the optimal while executing among two and seven orders of magnitude faster and with applicability to scenarios with thousands and hundreds of thousands of energy demands.
... This paper suggests that residential customers participate in the PTR program to avoid the penalties and use home energy management system (HEMS) to intelligently and automatically perform the load-shedding. HEMS executes monitoring and controlling of smart household appliances and information devices [7][8][9][10][11][12][13][14][15][16]. User-friendly HEMS can meet the electricity demands based on a user's setting [11]. ...
... The constraints of the objective function are expressed by Equations (6)- (10). As per Equation (6), the planned DR should be greater than the target DR at each period to prevent under performance of the DR. ...
... where DR enable (n, t ): The user-defined DR permission of plug n at period t. (10) where n sh : The number of shaded of plug n. This objective function is non-linear programming which can be used to determine the optimal scheduling of appliances taking the shedding limitations of different appliances into consideration [13]. ...
Abstract This paper presents the optimal load‐shedding strategy of home energy management systems (HEMSs). The HEMS uses the dynamic programming (DP) method to plan the day ahead, schedule, and connects to smart plugs to perform load‐shedding. HEMS solves the shedding schedule of smart plugs considering the prediction of consumption of each smart plug and the consumer's demand response (DR) setting. In addition, the maximum DR capacity was determined by using golden section search (GSS). Some DR experiments were conducted by the proposed method in laboratory and these comprised three different DR durations. The results show that the estimated DR capacities and shedding schedules were feasible. Thus, it helps both the DR participants and aggregator to seize the optimum performance and benefits.
... In [10], a distributed framework using the greedy iterative algorithm is proposed for scheduling the individual smart home user. Single utility with multiple users is considered in the scheduling scenario. ...
... In addition to that, it is also possible to sell back the excessive renewable power to the grid. Hence, the electricity consumption at in t th time slot as in (10). ...
Satisfying consumer’s electricity demand at peak hours is an important problem in smart grid. From the perspective of consumers, the residential electricity consumption scheduling would aim to minimize the electricity cost and also wish to maintain their comfort. In contrast, the power utilities concentrate on to flatten the peak loads in the electricity demand. In this paper, both the viewpoints are taken into consideration while scheduling the residential appliances in smart grid. The Self Adaptive Mutated Particle Swarm Optimization Algorithm is proposed for solving the above problem. Simulations have been carried out and the results are compared with the Non-Dominated Sorting Genetic Algorithm II. From the results obtained, it is clearly proved that the proposed algorithm provides better schedules for the smart home, with minimized electricity cost and least Peak-to-Average Value whereas maximizing the user comfort. Moreover, the proposed algorithm shows its effectiveness with the increase in problem size.
... In essence the equilibirium price is found such that: (i) every household maximize his/her utility given prices, and (ii) markets clear (i.e. the total demand for each commodity just equals the aggregate endowment at every time t). For those reasons we will use Walrasian equilibrium (also known as the competitive equilibrium) to find the MG equilibrium price [41], [42]. In short, the key result of the Walrasian Equilibrium theory is the fundamental first welfare theorem. ...
... Lemma 1: First Welfare Theorem [42]. If the tuple (price, {load} N 1 ) where N is the total number of households in the MG forms a Walrasian Equilibrium then the load matrices {load} N 1 are pareto-optimal. ...
The current electricity market is better described as an oligopoly than a market of perfect competition from which, in fact, it may be rather far. The increasing penetration of residential distributed energy resources has led to a significant number of prosumers in the electricity market. Microgrid community, a group of single controllable entity prosumers, is a promising component of the smart grid which will potentially yield a free electricity market. In this paper, we present a novel formation for a residential community microgrid that includes a coalition of prosumer households with solar photovoltaic systems. These households are connected through a virtual power bank that consists of households’ storage batteries and that mediates the communications between the households and the main grid. Using an application of mean field game theory, we find Nash Equilibrium strategies under which such sharing could minimize a linear combination of the households’ energy generation cost, energy consumption cost and revenue of sold energy. The resulting approach is tested on a case study of a constructed community micro-grid in Montreal, Quebec, Canada. The proposed mean field game approach can help decrease the aggregated cost and the individual energy cost. A comparative analytical study on the benefit of sharing was also performed, demonstrating that each prosumer is expected to have at least a 40 percent reduction on their individual cost if belonging to a microgrid community of 100 prosumers located in Montreal city.
... It achieved a reduction in peak and cost, but it failed to achieve a reduction in power consumption with the consumer waiver load demand. In [25], a greedy iterative algorithm was applied to schedule home devices, taking into account the combined load. It features low cost and total use without affecting consumer comfort. ...
... When a user selects pricing planned such as RTP, it means that the consumer is the amount of saving (P h -W s )ΔQ H , where P h is the energy rate and requests of the applicant, Q H is elec-tricity unit. Equation (25) shown the RT P energy rate P RTP is set between price of the market and W s charges of the standard energy. ...
This paper presents a new application of the sine cosine algorithm (SCA) to obtain optimal home energy management systems (HEMS) that use the load shifting strategy of demand side management (DSM) to optimize the energy consumption patterns of a smart home. It aims to manage the load demand in an efficient way to decrease electricity bills and peak to average ratio (PAR) while maintaining user comfort through coordination among home devices. In order to meet the load demand of electricity consumers, this study schedules the load on a day-ahead and real-time basis. The main objective of the paper is to help balance the load through ON-peak hours and OFF-peak hours. Three price signals, namely: real-time pricing (RTP), time of use (TOU), and critical peak pricing (CPP), are used to evaluate the proposed algorithm. The results show that the electricity bill and PAR are minimized by up to 40% and 50%, respectively. The scheduling using coordination between devices in real-time does not significantly impact the cost of electricity and the peak to average ratio.
... Smart-home loads can be divided according to their operating nature into two categories: schedulable and nonschedulable loads. Non-schedulable loads are operated occasionally according to the homeowner's desires without any predictable operating patterns, such as printers, televisions and hairdryers, whereas schedulable loads have a predictable operating pattern that can be shifted or controlled via SHEMS, such as washing machines and air conditioners [19]. According to [19], controllable devices are also classified into interruptible and non-interruptible load according to the effect of supply interruption on their tasks. ...
... Non-schedulable loads are operated occasionally according to the homeowner's desires without any predictable operating patterns, such as printers, televisions and hairdryers, whereas schedulable loads have a predictable operating pattern that can be shifted or controlled via SHEMS, such as washing machines and air conditioners [19]. According to [19], controllable devices are also classified into interruptible and non-interruptible load according to the effect of supply interruption on their tasks. Electric vehicles (EVs) can be considered as an exceptional load [20,21]. ...
Decentralized distributed clean-energy sources have become an essential need for smart grids to reduce the harmful effects of conventional power plants. Smart homes with a suitable sizing process and proper energy-management schemes can share in reducing the whole grid demand and even sell clean energy to the utility. Smart homes have been introduced recently as an alternative solution to classical power-system problems, such as the emissions of thermal plants and blackout hazards due to bulk plants/transmission outages. The appliances, sources and energy storage of smart homes should be coordinated with the requirements of homeowners via a suitable energy-management scheme. Energy-management systems are the main key to optimizing both home sources and the operation of loads to maximize home-economic benefits while keeping a comfortable lifestyle. The intermittent uncertain nature of smart homes may badly affect the whole grid performance. The prospective high penetration of smart homes on a smart power grid will introduce new, unusual scenarios in both generation and loading. In this paper, the main features and requirements of smart homes are defined. This review aims also to address recent proposed smart-home energy-management schemes. Moreover, smart-grid challenges with a high penetration of smart-home power are discussed.
... The appliance operation can be suspended between phases but cannot be interrupted in the middle of a phase [38]. The second approach is to refrain from subdividing the appliance's run time into uninterruptible phases and to allow interruptions at any time [39,40]. The first approach is adopted in our study. ...
Forecast uncertainties pose a considerable challenge to the success of model predictive control (MPC) in buildings. Numerous possibilities for considering forecast uncertainties in MPCs are available, but an in-depth comparison is lacking. This paper compares two main approaches to consider uncertainties: robust and stochastic MPC. They are benchmarked against a deterministic MPC and an MPC with perfect forecast. The MPCs utilize a holistic building model to reflect modern smart homes that include photovoltaic power generation and storage, thermally controlled loads, and smart appliances. Real-world data are used to identify the thermal building model. The performance of the various controllers is investigated under three levels of uncertainty for two building models with different envelope performance. For the highly insulated building, the deterministic MPC achieves satisfactory thermal comfort when the forecast error is medium or low, but the thermal comfort is compromised for high forecast errors. In the poorly insulated building, thermal comfort is compromised at medium and high forecast errors. Compared to the deterministic MPC, the robust MPC increases the electricity cost by up to 4.5% and provides complete temperature constraint satisfaction while the stochastic MPC increases the electricity cost by less than 1% and fulfills the thermal comfort requirements.
... In smart grid solutions (SGs), distributed energy management systems (EMSs) are commonly employed [56]. Several distributed systems, such as home energy management systems [57,58], smart city districts, and smart cities, are attempting to optimize energy scheduling. For instance, in reference [59], SGs are optimized to meet various load requirements and DERs, and reference [56] proposes a platform for smart buildings. ...
Energy shortage is one of the major concerns in today’s world. As a consumer of electrical energy, the electric railway system (ERS), due to trains, stations, and commercial users, intakes an enormous amount of electricity. Increasing greenhouse gases (GHG) and CO2 emissions, in addition, have drawn the regard of world leaders as among the most dangerous threats at present; based on research in this field, the transportation sector contributes significantly to this pollution. Railway Energy Management Systems (REMS) are a modern green solution that not only tackle these problems but also, by implementing REMS, electricity can be sold to the grid market. Researchers have been trying to reduce the daily operational costs of smart railway stations, mitigating power quality issues, considering the traction uncertainties and stochastic behavior of Renewable Energy Resources (RERs) and Energy Storage Systems (ESSs), which has a significant impact on total operational cost. In this context, the first main objective of this article is to take a comprehensive review of the literature on REMS and examine closely all the works that have been carried out in this area, and also the REMS architecture and configurations are clarified as well. The secondary objective of this article is to analyze both traditional and modern methods utilized in REMS and conduct a thorough comparison of them. In order to provide a comprehensive analysis in this field, over 120 publications have been compiled, listed, and categorized. The study highlights the potential of leveraging RERs for cost reduction and sustainability. Evaluating factors including speed, simplicity, efficiency, accuracy, and ability to handle stochastic behavior and constraints, the strengths and limitations of each optimization method are elucidated.
... This oversight has created problems in managing energy consumption during peak and non-peak hours. Additionally, the authors in Gao et al. (2014); Vardakas et al. 2016;Bradac et al. 2015;Adika and Wang 2014;Chavali et al. 2014) did not prioritize consumer convenience in their studies. In Zhou et al. (2016), the authors analyze and review house devices and HEMS infrastructure in smart homes, as well as the use of various renewable energy sources such as solar, geothermal, biomass, and wind. ...
In this study, an improved bald eagle search optimization algorithm (IBES) is utilized to develop home energy management systems for smart homes. This research is crucial for energy field researchers who are interested in optimizing energy consumption. The primary objective is to optimally manage load demand, reduce the average peak ratio, lower electricity bills, and enhance user comfort. To accomplish this goal, the load conversion strategy is used to coordinate household appliances and manage the home power system effectively. This approach aims to minimize peak–average ratios and electricity costs while ensuring consumer convenience. To minimize electricity bills, the study schedules the consumer’s daily activities based on actual time and next day’s energy demand. Furthermore, a fitness criterion is used to balance the load between off-peak and on-peak hours. The scheduler is designed to achieve an optimal device on/off state that minimizes device waiting time by coordinating household appliances in real time. To address the background problem of real-time rescheduling, dynamic programming is employed. The study evaluates the modified algorithm’s performance using three pricing strategies: critical peak pricing, real-time pricing, and time of use. The modified IBES technique is utilized to achieve the specified objectives of minimizing the electricity bill, reducing the peak–average ratio, and enhancing user convenience.
... Based on this, in the last few years, with the expansion of this field, many researchers are focusing on developing strategies and mechanisms to solve such problems [4,5]. For instance, in Chavali et al. [6], the authors developed a distributed algorithm for scheduling appliances in a Home Energy Management System (HEMS) to monitor and control energy consumption using a Demand Response (DR) approach to encourage the consumers to modify their consumption patterns in response to time-varying electricity prices. ...
Reducing our carbon footprint is one of the biggest challenges facing humanity in the current millennium. In the last few years, researchers have focused their attention on balancing the demand and supply, thereby allowing better management of renewable energy resources. In this regard, many energy management strategies have been developed. Nevertheless, testing, evaluating, and comparing such approaches in multiple scenarios, and above all, assessing their generalization, is currently a hard, or even impossible, task. Furthermore, analyzing the impact of such strategies in Energy Communitys (ECs) is an underexplored task. This is due to the lack of existing EC datasets and simulators that allow users to evaluate and compare their approaches. Although there are some tools to generate demand and production profiles, they are all developed with a single purpose. To address these challenges, PROCSIM is presented: an open-source simulator designed especially to create energy community datasets for multiple purposes—in particular, to test and evaluate different algorithms and models. It includes integration with a consumption-profiles generator, tools to simulate Solar Photovoltaic (PV) and wind production, a module that generates an EC dataset, and finally, a set of metrics to evaluate the generated community. To conclude, a case study comprised of two experiments is presented. The first experiment shows how an EC dataset can be created using PROCSIM. In the second experiment, an exemplification of how this dataset can be used to evaluate an optimization algorithm is provided, namely, to optimize the control of a battery. Ultimately, it is shown that the simulator can generate energy community power demand and generation scenarios. The scenarios can be fully customized by the user, considering different sizes (power capacity) and numbers of assets, and diverse generation/consumption characteristics. The datasets generated by PROCSIM can be useful for different purposes, such as optimal scheduling of EC generation resources and consumption flexibility, and for designing battery energy storage systems.
... Numerous researchers have formulated distributed CEMS. For instance, [24] proposed a distributed CEMS that allows every HEMS in a community to iteratively adjust their appliance scheduling, but with a penalty term penalizing large changes in scheduling between consecutive iterations. In [25], a distributed CEMS was proposed to modify the aggregated load profiles in a community while minimizing resident payments without affecting their comfort nor compromising their privacy. ...
This study presents a resident-centric distributed community energy management system (CEMS). More specifically, the proposed resident-centric distributed CEMS allows residents to schedule their appliances autonomously, without the need to collaborate with the community and to consider whether their appliance scheduling is optimal from the perspective of the entire community. The central controller in the proposed CEMS will then determine a solution that is optimal for the entire community by dispatching the community's distributed energy sources according to the appliance scheduling of residents. In other words, the proposed distributed resident-centric CEMS allows residents to act autonomously while securing the collective goals of the community to a certain extent. In this paper, the collective goals of the community include participating in incentive-based demand response (IBDR) events at a specific time interval, and decreasing the total electricity cost of the community in response to time-varying electricity prices. The proposed distributed resident-centric CEMS is developed using the concept of distributed optimization and mixed-integer linear programming. Different types of public loads are incorporated into the proposed framework including stoppable and deferrable public loads. The simulation results show that the proposed framework dispatches power optimally.
... A distributed structure allows end users to schedule their loads individually while communicating with a central entity to obtain information about neighboring electricity profiles. Community energy management can be decomposed into a two-level optimization problem, in which the upper level seeks to flatten the system load profile and the lower level minimizes individual residential users' energy costs [15], [16]. ...
Price-based demand response (DR) can aid power grid management, but an uncoordinated response may lead to peak rebounds during low-price periods. This article proposes a community energy management system based on multiagent reinforcement learning. The scheme consists of a community aggregator that optimizes the total community electricity cost for multiple residential users. A home requires energy management for home appliances, electric vehicles, energy storage systems, and renewable energy generation. The appliance scheduling problem is decomposed into smaller sequential decision problems that are easier to solve. Renewable generation is predicted and used to mitigate the influence of energy generation uncertainty. As indicated in numerical analyses, the proposed approach can handle the uncertainty in renewable energy and leads to more economical energy usage relative to existing energy management methods. The method outperforms conventional algorithms, such as centralized mixed-integer nonlinear programming and genetic algorithm-based optimization, in terms of mitigating peak rebounds and addressing the uncertainty of renewable energy generation.
... Load distribution has been modified from high peak hours (1-5 and 19-21) to low and middle peak (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18) hours. Accordingly, the electricity cost and high peaks have been minimized as indicated by Figure 8. ...
... In other respects, the progress of communication technologies allows the consumer to access all devices [10]. The consumers are enabled to plan the working schedules of indoor devices with these systems [11]. However, because the resident uses a significant part of the house devices in an uncontrolled and arbitrary manner, sudden energy demand appears, and energy management systems cannot control it [12]. ...
... 中 国 电 机 工 程 学 报 第 31 卷 厂的对外特性，但其内部的运行控制方式与传统电 厂截然不同 [2] ，需在快速变化的运行环境下协同多 元异构、产权分散的 DERs，达到上级电网要求的 调节目标，同时确保其自身始终处于安全、优质、 经济的运行状态。因此，研究 VPP 内部的优化运 行策略是其参与大电网调度的重要基础。 当前诸多研究采用集中式或分布式优化的方 法协调 DERs 实现经济运行。在集中式优化方面， 文献 [3]建立了基于机会约束规划的 VPP 优化模型； 文献 [4]提出了考虑热电联合调度的 VPP 鲁棒优化 模型。相比于集中控制，基于分布式优化的运行策 略在计算负担与隐私保护等方面有更好的性能。在 此方面，文献 [5]提出了分布式优化方法实现 VPP 与常规机组的协同运行，但尚不涉及 VPP 内部 DERs 的协同； 文献 [6]考虑了多 VPP 分布式联合优 化，但 VPP 内部运行仍为集中式控制。为进一步 实现 DERs 的解耦， 文献 [7]和 [8]以微电网为背景提 出基于一致性的 DERs 的分布式协调算法；文献 [9] 和 [10]提出基于交替方向叉乘子法的设备层级分布 式优化方法；文献 [11]研究了 VPP 优化运行中分布 式算法的步长自适应调整方法，有效确保收敛效 果；文献 [12]研究了基于元模型的主从博弈均衡算 法，可以实现多利益主体的自主决策。 上述以分布式优化算法为内核的 VPP 优化方 法虽然在一定程度上实现了空间解耦，但节点之间 需反复迭代信息，在获得数值收敛后 DERs 方可执 行运行策略，难以适用于快速变化的运行环境。首 先，温度、辐照、刚性负荷等变化迅速，当分布式 优化算法达到数值收敛时，VPP 的运行状态、可行 域等可能较启动算法时已发生改变，此时得出的最 优解可能并非实际中的最佳策略，甚至违背运行可 行域 [13] ；其次，VPP 内部通讯资源有限，频繁的信 息交互将面临数据丢包、时延等问题 [14] ；此外， DERs 是典型的用户资产， 需设计合理的激励机制， 但传统的分布式优化算法将带来类似讨价还价的 过程， 如文献 [15] ...
Virtual Power Plant (VPP) needs to coordinate numerous distributed energy resources (DERs) to meet the regulation requirements of the upstream power grid in a rapidly changing operating condition. However, most of the current literature on the optimal operation VPP consumes much time at every time slot for computation and communication, which is not suitable in a time-varying condition. The self-approaching optimization (SAO) provides a novel scheme for the VPP operation , which claims the fast optimum tracking with the fine -grained time interval. To achieve this scheme, this paper proposed an online distributed optimization (ODO)-based SAO operation for VPP. First, the generic operation model is formulated considering non-storage and energy storage DERs. Then, the SAO algorithm is derived via relaxation and transformation for tackling the issues of spatiotemporal coupling, fast updating of variables, and incentive schemes for self-responding. Moreover, the gap of SAO is presented to characterize the loss of optimum. Also, the upper bound of the gap of SAO is analytically proved. Photovoltaics, energy storage, and electric vehicles are involved in the test systems. The benchmark tests are performed to corroborate the advantages of the SAO on satisfying constraints, optimizing, and computation efficiency.
... In view of that large-scale distribution network may include a large number of distributed energy resource, which will make centralized algorithm cannot possess quick response ability and adaptive capacity any more [41]. Thus, the distributed algorithm is used to overcome the disadvantages of the centralized algorithm. ...
The hybrid algorithm strategy proposed in this paper aims to combine the optimal power flow with voltage-var optimization to meet the load demand, reduce the transmission line losses and maintain the voltage within a practicable range. A distributed neural network algorithm is used to seek an optimal solution of active power flow which minimizes the cost of active power. In order to ensure that the optimal power flow will not cause a serious impact to the stability of the power grid, voltage-var optimization engines which employ a multi-algorithm coordination are presented to optimize the losses of power grid and the bus voltage. The simulation of IEEE 30-bus shows that the proposed hybrid algorithm strategy can not only minimize the cost of active power generation, but also satisfy the load demand under the precondition that all the bus voltage is within the reference range. The percentages of power losses comparisons verify that the proposed hybrid algorithm strategy can decrease the transmission line losses of the power grid effectively, which will not bring a serious influence to the stability of the power grid.
... Three classes of consumers are proposed within this study, and categorized according to consumers' priorities [7,8]. ...
Recently, Demand Side Management (DSM) has played an important role in the smart grid through the optimization of residential load consumption. Smart DSM is a very important tool that permits customers to take right decisions for their energy consumption, it also helps the energy utilities to decrease the over load demand and reshape the load curve. This paper proposes an optimized DSM technique based on smart metering to minimize load consumption, especially during load peaks. Bat Algorithm technique is proposed to optimize the minimum consumption during peak hours according to load type for three consumers' types whom are classified based on their lifestyles. A control algorithm is applied to the proposed system to achieve load shifting according to the optimization results.
... The power price curve presented in this paper is plotted using the variable ratio [43] as follows: γ e,t P e,t = χ e,t C S e,t P e,t = χ e,t 2a e P e,t + b e (19) γ g,t P g,t = χ g,t C S g,t P g,t = χ g,t 2a g P g,t + b g (20) γ eini,t (1 − 0.12) ≤ γ e,t P e,t ≤ γ eini,t (1 + 0.15) (21) γ gini,t (1 − 0.12) ≤ γ g,t P g,t ≤ γ gini,t (1 + 0.15) ...
The coordinated implementation of demand response technology and dynamic energy prices facilitates the interaction among multiple stakeholders in the smart integrated energy system. To achieve the optimal operation between different entities in the system, this paper proposes a multi-objective optimization model for smart integrated energy system considering demand responses and dynamic prices that reflects the preferences of multiple stakeholders. Based on the tightly coupled characteristics of a multi-energy system, a flexible two-dimensional demand response model with spatio-temporal coupling characteristics is established. By analyzing the characteristics of multi-entities joint pricing, the dynamic energy price formulated with the participation of both supplier and demander is optimized, and the dynamic price control strategy of different stakeholders under different benefit weights is obtained. A case study verifies the effectiveness of the proposed method. According to the interest preferences of different entities, different strategies and operating mechanisms can be derived, which is conducive to improving the economy and reliability of the operation of the smart integrated energy system and promoting the interaction between multiple entities.
... The algorithm can control selected appliances and keep the total household power consumption below a certain limit while considering customer preferences and allowing the customer more flexibility to operate their appliances. A distributed algorithm for a HEM system is implemented in [9]. This algorithm finds the optimal operating times for the electric appliances and their corresponding energy consumptions by minimizing the overall cost operation. ...
This paper presents intelligent energy management with penetration of Distributed Generation (DG) and Electric Vehicles (EVs). The envisaged problem is a hard combinatorial Mixed-Integer Linear Programming (MILP) problem due to the continuous, discrete, and binary variables. The proposed problem focuses on minimizing the electricity cost. The MILP problem is modelled with a deterministic technique, namely TOMLAB, using a CPLEX solver. This paper includes a realistic case study using data collected from two real buildings facilities (consumption and generation profiles).
... For example, once a month, a heating cycle above 60 • C is completed in the electric boiler to eliminate possible Legionella outbreaks. This scheduling pursues the reduction of the consumption of household appliances and the shifting of loads (shifting to optimize expenditure and their optimal time of operation) to reduce electricity billing [72][73][74] and maintain or increase the comfort of residents [73,75]. Regarding billing optimization, the appliance scheduling technique based on mathematical optimization is suitable for small-sized problems such as individual dwellings instead of other less demanding techniques for larger problems, as we will discuss later. ...
In recent years, interest in home energy management systems (HEMS) has grown significantly, as well as the development of Voice Assistants that substantially increase home comfort. This paper presents a novel merging of HEMS with the Assistant paradigm. The combination of both concepts has allowed the creation of a high-performance and easy-to-manage expert system (ES). It has been developed in a framework that includes, on the one hand, the efficient energy management functionality boosted with an Internet of Things (IoT) platform, where artificial intelligence (AI) and big data treatment are blended, and on the other hand, an assistant that interacts both with the user and with the HEMS itself. The creation of this ES has made it possible to optimize consumption levels, improve security, efficiency, comfort, and user experience, as well as home security (presence simulation or security against intruders), automate processes, optimize resources, and provide relevant information to the user facilitating decision making, all based on a multi-objective optimization (MOP) problem model. This paper presents both the scheme and the results obtained, the synergies generated, and the conclusions that can be drawn after 24 months of operation.
... Recently, many researchers have drawn their attention to reschedule building assets in order to reduce their total energy costs, proposing frameworks for appliance scheduling based on cost minimization. In their model, each user in the system would find an optimal start time and operating mode for the appliances in response to the varying electricity prices [10][11][12]. Meanwhile, other researchers have taken consumer convenience and satisfaction into account while minimizing the total energy cost. ...
The impact of load growth on electricity peak demand is becoming a vital concern for utilities. To prevent the need to build new power plants or upgrade transmission lines, power companies are trying to design new demand response programs. These programs can reduce the peak demand and be beneficial for both energy consumers and suppliers. One of the most popular demand response programs is the building load scheduling for energy-saving and peak-shaving. This paper presents an autonomous incentive-based multi-objective nonlinear optimization approach for load scheduling problems (LSP) in smart building communities. This model’s objectives are three-fold: minimizing total electricity costs, maximizing assigned incentives for each customer, and minimizing inconvenience level. In this model, two groups of assets are considered: time-shiftable assets, including electronic appliances and plug-in electric vehicle (PEV) charging facilities, and thermal assets such as heating, ventilation, and air conditioning (HVAC) systems and electric water heaters. For each group, specific energy consumption and inconvenience level models were developed. The designed model assigned the incentives to the participants based on their willingness to reschedule their assets. The LSP is a discrete–continuous problem and is formulated based on a mixed-integer nonlinear programming approach. Zoutendijk’s method is used to solve the nonlinear optimization model. This formulation helps capture the building collaboration to achieve the objectives. Illustrative case studies are demonstrated to assess the proposed model’s effect on building communities consisting of residential and commercial buildings. The results show the efficiency of the proposed model in reducing the total energy cost as well as increasing the participants’ satisfaction. The findings also reveal that we can shave the peak demand by 53% and have a smooth aggregate load profile in a large-scale building community containing 500 residential and commercial buildings.
... As one of the key characteristics of smart grid, demand response (DR) is described as an effective way to induce power consumers to alter their power demand from peak hours to off-peak hours within a day [5e7]. By adjusting the shiftable load, DR programs can reduce the peak-to-average ratio (PAR) to maintain the stability of the power grid [8] and avoid the cost of backup generators [9]. Therefore, it is of vital importance for power grid operators to establish an effective DR mechanism to enhance the flexibility of the power system [10]. ...
This paper proposes a hybrid demand response mechanism considering three types of participants: power grid operator (PGO), retailers and end users. Different from the traditional price-based or incentive-based methods, this hybrid mechanism combines real-time pricing and real-time incentive together to implement demand response programs dispatched by PGO, i.e., the PGO provides incentives to retailers and the retailers set optimal real-time prices to users every 5 minutes. This hybrid DR mechanism can better motivate retailers to participate by providing them with monetary incentives from PGO for load shifting. We use a three-level Stackelberg game to model the proposed mechanism. The PGO first determines the optimal incentive rate to minimize its cost, then the retailers decide the optimal electricity price to maximize their profits, and the users finally choose the optimal power demand to maximize their welfare. The analytical solutions of the optimal decisions for every participant are given. We also propose a distributed algorithm to implement this mechanism in a practical application by considering information asymmetry. The simulation results verify its advantages over traditional demand response mechanisms.
... Load shifting is a promising solution that lowers PAR and electricity bills. To maintain user convenience, home appliances are categorized and scheduled in many ways as can be seen in Cetin et al., (2014) and Chavali et al. (2014). Authors in Jhanjhi et al. (2018) pointed out emission reduction and preserving energy generated by fossil fuels. ...
Fusion of Information and Communication Technologies (ICT) in traditional grid infrastructure makes it possible to share certain messages and information within the system that leads to optimized use of energy. Furthermore, using Computational Intelligence (CI) in the said domain opens new horizons to preserve electricity as well as the price of consumed electricity effectively. Hence, Energy Management Systems (EMSs) play a vital role in energy economics, consumption efficiency, resourcefulness, grid stability, reliability, and scalability of power systems. The residential sector has its high impact on global energy consumption. Curtailing and shifting load of the residential sector can result in solving major global problems and challenges. Moreover, the residential sector is more flexible in reshaping power consumption patterns. Using Demand Side Management (DSM), end users can manipulate their power consumption patterns such that electricity bills, as well as Peak to Average Ratio (PAR), are reduced. Therefore, it can be stated that Home Energy Management Systems (HEMSs) is an important part of groundbreaking smart grid technology. This article gives an extensive review of DSM, HEMS methodologies, techniques, and formulation of optimization problems. Concluding the existing work in energy management solutions, challenges and issues, and future research directions are also presented.
... In terms of continuity of operation time [18], the schedulable appliances can be further classified into interruptible and non-interruptible. In general, interruptible appliances are most often schedulable. ...
Energy management strategies are instrumental in the performance and economy of smart homes integrating renewable energy and energy storage. Home energy management system (HEMS) in the smart home allows the customer to control, optimize and monitor the energy consumption and the energy conservation. In this paper, a brief overview on the architecture and functional modules of smart HEMS is presented. Then, the advanced HEMS infrastructures and home appliances in smart houses are thoroughly analyzed and reviewed. For management and monitoring the energy consumption of home appliances and lights is used ZigBee based energy measurement modules while for renewable energy is used a Power Line Communication (PLC) based renewable energy gateway. The home server monitors and controls the energy consumption and generation and controls the home energy use to reduce the energy cost. The remote energy management server aggregates the energy information from the home servers, compares them and creates statistical analysis information. We propose the control algorithm to efficiently manage the renewable energy and storage to minimize grid power costs at individual home. The proposed HEMS architecture is expected to optimize home energy use and result in home energy cost saving.
... A distributed framework for the demand response based on cost minimization has been introduced in [13]. The authors propose a distributed energy scheduling algorithm for demand response in the smart grid setting. ...
The need for higher energy efficiency and
demand response program reshape existing electrical networks
which results in the smart power systems grid. Modern
electrical networks are also becoming more careful than ever
to keep the greenhouse gas emission under acceptable limits.
Demand-side residential load scheduling promotes smart grid
consumers to change their electricity consumption responding
to electricity price variations. In this paper, a system has been
designed for efficient demand-side management in the smart
grid. This research mainly focuses on optimal load scheduling
for minimizing energy costs and reducing peak loads. The
demand response technique uses specifically to perform load
scheduling optimization to minimize the total cost of power
consumption of residential household loads while maintaining
the expected satisfaction. A detailed load scheduling model has
been developed for enabling the Home energy management
system (HEMS) to compute optimized power consumption
while incorporating customers’ satisfaction and dynamic price
variation. The results showed the proposed dynamic pricing based HEMS significantly reduces 38.80% of peak loads and
minimizing 22.55% of total energy costs for customers.
In this paper, we focus on modeling and analysis of demand-side management in a microgrid where agents utilize grid energy and a shared battery charged by renewable energy sources. We model the problem as a generalized stochastic dynamic aggregative game with chance constraints that capture the effects of uncertainties in the renewable generation and agents’ demands. Computing the solution of the game is a complex task due to probabilistic and coupling constraints among the agents through the state of charge of the shared battery. We investigate the Nash equilibrium of this game under uncertainty considering both the uniqueness of the solution and the effect of uncertainty on the solution. Simulation results demonstrate that the presented stochastic method is superior to deterministic methods.
The evolution of the smart grid has enabled residential users to manage the ever-growing energy demand in an efficient manner. The smart grid plays an important role in managing this huge energy demand of residential households. A home energy management system enhances the efficiency of the energy infrastructure of smart homes and provides an opportunity for residential users to optimize their energy consumption. Smart homes contribute significantly to reducing electricity consumption costs by scheduling domestic appliances effectively. This residential appliance scheduling problem is the motivation to find an optimal appliance schedule for users that could balance the load profile of the home and helps in minimizing electricity cost (EC) and peak-to-average ratio (PAR). In this paper, we have focused on appliance scheduling on the consumer side. Two novel home energy management models are proposed using multiple scheduling options. The residential appliance scheduling problem is formulated using the multiple knapsack technique. Serial and parallel scheduling algorithms of home appliances namely MKSI (Multiple knapsacks with serial implementation) and MKPI (Multiple knapsacks with parallel implementation) are proposed to reduce electricity cost and PAR. Price-based demand response techniques are incorporated to shift appliances from peak hours to off-peak hours to optimize energy consumption. The proposed algorithms are tested on real-time datasets and evaluated based on time of use pricing tariff and critical peak pricing. The performance of both the algorithms is compared with the unscheduled scenario and existing algorithm. Simulations show that both proposed algorithms are efficient methods for home energy management to minimize PAR and electricity bills of consumers. The proposed MKSI algorithm achieves cost reduction of 20.26% and 42.53% for TOU and CPP, respectively as compared to the unscheduled scenario while PAR is reduced by 45.07% and 39.51% for TOU and CPP, respectively. The proposed MKPI algorithm achieves 22.33% and 46.36% cost reduction compared to the unscheduled case for TOU and CPP while the PAR ratio is reduced by 46.47% and 41.16% for TOU and CPP respectively.
Forecast uncertainties pose a considerable challenge to model predictive controllers (MPCs) in buildings. Numerous possibilities for considering forecast uncertainties in MPCs are available, but an in-depth comparison between those is still lacking. The present paper compares two main approaches to consider uncertainties: robust and stochastic MPC. They are benchmarked against a deterministic MPC and an MPC that perfectly predicts the future. The MPCs utilize a holistic building model to reflect modern smart homes, including photovoltaic power generation and storage, thermally controlled loads, and smart appliances. Real-world data is used to identify the thermal building model. The performance of the various controllers is investigated under three different uncertainty scenarios. The results show that the stochastic MPC performs superior to the robust and deterministic MPC. However, the deterministic MPC yields sufficiently satisfying results for our holistic building model and it is easier to be implemented.
An aggregation scheme is an effective transactive manner of Distributed Energy Resources (DER) spreading across distribution networks. Distributed approach locally achieves cost minimization of an aggregator and customers. The uncertainties of wholesale market price and rooftop PV output will impact on aggregator's scheduling decision and each customer's cost, while solar energy fluctuation can cause an overvoltage problem in distribution networks. However, the probability distributions of these uncertainties always have errors, even in emerging data-based methods. There is no stochastic method using real data with an out-of-sample guarantee suitable for this distributed approach so far to help an aggregator avoid price risk and manage customers' energy against solar energy fluctuation. To address these unsolved issues, we propose a data-driven Wasserstein distributionally robust formulation of the aggregator's agent and customer's agent respectively. The Wasserstein metric is employed to construct the Wasserstein ambiguity set. The mathematical models are then reformulated equivalently to convex programming respectively so that the operating model can be solved by the off-the-shelf solver. To improve the efficiency of the distributed solving framework, an alternating optimization procedure (AOP) process is proposed to overcome the issue caused by binary variables in the alternating direction method of multipliers (ADMM). The proposed operation framework is verified on the modified IEEE 33-bus distribution network and realistic single-feeder LV network.
Due to its advantages and the continual availability of solar energy, photovoltaic (PV) systems have become the most popular energy production equipment in various business and residential structures. This chapter proposes solar radiation forecasting to manage solar power generation in residential and commercial buildings using deep learning algorithms. Convolutional neural network (CNN) and long short-term memory (LSTM) are two proposed algorithms created to forecast solar radiation to control the energy produced at a PV plant on the roof of the University of Macau in China. Climate data collected at the university’s meteorological station are used as input variables in solar radiation forecasting. The performance of each network is assessed using a variety of performance evaluation measures. Based on the results and analysis, the LSTM technique, which forecasts solar radiation with an accuracy of R = 99.84%, outperforms the CNN technique that predicts solar radiation with an accuracy of R = 99.71%. Furthermore, the LSTM technique’s predictions exhibit a lower forecasting error than the CNN process.KeywordsSolar energySolar radiation forecastingPhotovoltaic power generationBuildingsConvolutional neural network (CNN)Long short-term memory (LSTM)
Home energy management systems can be defined as systems responsible for monitoring and managing electricity demand to optimize energy consumption. According to a report by the US Energy Information Administration, by 2030, the gap between electricity generation and consumption will reach more than 2 billion GWH, of which 30% of the demand is for the domestic sector, where energy management systems play an essential role in bridging the gap between production and demand. With the expansion of smart homes and companies that work in this field, many activities have been done so far to improve consumption patterns, demand response, monitoring, and control of high-consumption equipment. Home energy management systems consist of five parts: monitoring, control, management, logging, and fault detection. All these components must be directly or indirectly connected to the power company, renewable energy sources, and all household appliances (controllable or uncontrollable). This chapter comprehensively describes home energy management systems and reviews all the existing infrastructure and technologies to achieve the mentioned goals.KeywordsHome Energy Management Systems (HEMS)Demand Response (DR)Smart gridsSmart homeIntegrated wireless technology
This article reviews energy management schemes for smart homes integrated with renewable energy resources in the context of the COVID-19 pandemic. The incorporation of distributed renewable energy system has initiated an acute transition from the traditional centralized energy management system to independent demand responsive energy systems. Renewable energy-based Smart Home Energy Management Systems (SHEMSs) play a vital role in the residential sector with the increased and dynamic electricity demand during the COVID-19 pandemic to enhance the efficacy, sustainability, economical benefits, and energy conservation for a distribution system. In this regard, the reviews of various energy management schemes for smart homes appliances and associated challenges has been presented. Different energy scheduling controller techniques have also been analyzed and compared in the COVID-19 framework by reviewing several cases from the literature. The utilization and benefits of renewable-based SHEMS have also been discussed. In addition, both micro and macro-level socio-economic implications of COVID-19 on SHEMSs are discussed. A conclusion has been drawn given the strengths and limitations of different energy scheduling controllers and optimization techniques in the context of the COVID-19 pandemic. It is observed that renewable-energy-based SHEMS with improved multi-objective meta-heuristic optimization algorithms employing artificial intelligence are better suited to deal with the dynamic residential energy demand in the pandemic. It is hoped that this review, as a fundamental platform, will facilitate the researchers aiming to investigate the performance of energy management and demand response schemes for further improvement, especially during the pandemic.
The widespread popularity of smart meters has promoted the transition from traditional grid into modern smart grid, which is aimed to meet the rapid-growing demand for higher quality service and take up the emergence of new challenges. How to analyze, transmit and make the most of massive smart meter data to enhance the efficiency and reliability is our priority. The purpose of this paper is to conduct a detailed review to summarize and evaluate the latest advances in smart meter data analysis, privacy preserving and residential energy management. We conclude the analysis of smart meter data, protecting techniques in the process of delivering and end-uses of smart meter data application according to the flowing direction of smart meter data. Compared with other review papers, we analyze the merits and drawbacks in corresponding situations and provide readers a more detailed eyesight to the research status in modern smart grid.
Time-varying electricity prices in demand response (DR) programs motivate users to change their consumption behavior with the aim of electricity bill cost minimization. Coordinated DR schemes aim to flatten the aggregate load profile and mitigate rebound peaks. However, a comprehensive model which integrates supply and demand sides has to be developed to consider system constraints and renewable energy resources. In this regard, this article proposes a bi-level framework for supply and demand side energy management in an islanded microgrid. In the first level, consumers schedule their appliances’ operation to minimize their expenses. In the second level, a dynamic economic dispatch problem is solved which finds the power generation schedule for the generators resulting in the minimum electricity production cost. A game-theoretic turn-based decentralized algorithm is suggested which manages the interaction between both levels. Simulation results show that the proposed algorithm minimizes users’ electricity bill costs while satisfying power system constraints.
Extensive research has been conducted in recent years on the development algorithm for optimal utilization of Home Energy Management Systems and the main goal is to determine the optimal applications for rechargeable appliances. This issue applies to Demand Response planning with Renewable Energy Sources and Energy Storage Systems. This thesis will present an improved algorithm for IoT-based HEMS that explores the algorithm, Demand Response, photovoltaic power, charge, and discharge rate of a parallel storage battery using multiple power supplies to charge the case. Consideration will be given to the need for hybrid home appliance planning to improve system performance parameters and to reduce load peaks, including load reduction plans. After conducting the necessary studies, various simulations are performed on the sample systems and the results will be analyzed and evaluated.
A smart grid is a new concept that provides a two-way information and electricity connection that has prepared an opportunity to involve customers for awareness of load profile, the participation of distributed generation, demand management, cost optimization, and variety in customer services. With the emergence of newer technologies in the fifth-generation (5G) and beyond, like the internet of things (IoT), devices tend to be always online. IoT provides the necessary platform for the exchange of information. Running demand-side management (DSM) needs technologies such as advanced metering infrastructure (AMI) on the consumption side. On the other hand, new devices can connect to the Internet. By sending a signal from the utility and receiving it by the AMI, a shiftable home appliance can run-in low-price time without human activity. This chapter focuses on providing an overview of the smart grid and the application of IoT in smart grids by focusing on DSM. Accordingly, well-known IoT algorithms based on DSM used in smart homes has been reviewed and investigated in this chapter. Accordingly, the basics of IoT and DSM and their application to the smart grids are investigated and reported in this study.
As a consequence of the continuous growth in the worldwide electricity consumption, supplying all customer electrical requests is becoming increasingly difficult for electricity companies. That is why, they encourage their clients to actively manage their own demand, providing several resources such us their Optimal Demand Profile (ODP). This profile provides to users a summary of the demand they should consume during the day. However, this profile needs to be translated into specific control actions first, such as the when each appliance should be used. In this article a comparison of the performance of two metaheuristic optimisation algorithms (Tabu Search and Estimation of Distribution Algorithm (EDA)) and their variants for the calculation of optimal appliance scheduling is presented. Results show that Tabu Search algorithm can reach better feasible solutions at faster execution times than EDA does.
Recently, home energy management systems (HEMS) are gaining more popularity enabling customers to minimize their electricity bill under time-varying electricity prices. Although they offer a promising solution for better energy management in smart grids, the uncoordinated and autonomous operation of HEMS may lead to some operational problems at the grid level. This paper aims to develop a coordinated framework for the operation of multiple HEMS in a residential neighborhood based on the optimal and secure operation of the grid. In the proposed framework customers cooperate to optimize energy consumption at the neighborhood level and prevent any grid operational constraints violation. A new price-based global and individualized incentives are proposed for customers to respond and adjust loads. The individual customers are rewarded for their cooperation and the network operator benefits by eliminating rebounding network peaks. The alternating direction method of multipliers (ADMM) technique is used to implement coordinated load scheduling in a distributed manner reducing the computational burden and ensure customer privacy. Simulation results demonstrate the efficacy of the proposed method in maintaining nominal network conditions while ensuring benefits for individual customers as well as grid operators.
Several load scheduling optimization models have been proposed for smart grid systems. Nevertheless, most of the existing models assume the presence of a pricing mechanism such as time of use (TOU) and consider single objective functions that are suitable for application under specific conditions. This paper proposes a generic load scheduling optimization model that can be employed under different operating conditions and can handle different scheduling criteria. In particular, the model considers TOU and allows the integration of distributed renewable energy systems (DRES). In addition, the load scheduling model is solved using the Cuckoo optimization algorithm. Performance of the Cuckoo algorithm is validated by formulating and solving an equivalent MILP model to the load scheduling problem. A set of experiments is designed to compare optimality and time performance of the Cuckoo and its equivalent MILP model. The Cuckoo results has shown superior or at least comparable results to the published results in the literature. A case study has been performed using real data taken from an academic building in Egypt to demonstrate the model applicability under different conditions. The results show that the building under specific conditions can achieve energy cost savings that range from 57% to 80%. The results have also provided useful managerial implications.
This paper addresses the provision of ancillary services in smart energy systems. A large number of prosumers are aggregated by an Energy Service Provider (ESP) in order to provide a manual Frequency Restoration Reserve (mFRR) service, which consists in offering some degree of flexibility and be willing to provide a power variation over a given time interval upon reception of an explicit manual request by the Transmission System Operator (TSO). The main focus of this paper is to define how the ESP can optimally distribute the requested flexibility effort to the prosumers in the pool, promptly providing the agreed mFRR service upon request of the TSO. In particular, a scalable strategy is proposed, able to account for integer decision variables like on/off commands, while reducing the combinatorial complexity of the problem and preserving privacy of local information via distributed computations. Lead and rebound effects are avoided by maintaining the originally scheduled energy exchange profile before and after the time interval where the TSO request must be satisfied. The simulation results show the effectiveness of the proposed approach in terms of scalability and quality of the obtained feasible solution.
Smart home energy management (SHEM) with residential photovoltaic (PV)-battery systems is a complicated issue with different facets. An integrated SHEM model covering the essential functions is missing. Meanwhile, residential PV-battery systems' optimal operations with renewable energy exchanges and imperfect forecasts are still open challenges. In this study, the research activities in SHEM are firstly organized by a pyramid with four functional layers: (i) Monitoring; (ii) Analyzing and forecasting; (iii) Scheduling; and (iv) Coordinating, which can serve as a standard pathway for developing SHEM. Second, guided by the pyramid taxonomy, an integrated SHEM model is developed for residential houses with PV-battery systems. Assuming a perfect Monitoring layer, we obtain the probabilistic load/PV forecasts and user preference vectors of shiftable appliances based on historical data. Then, we develop a two-stage stochastic programming model for optimal scheduling of single houses with a grid-connected PV-battery system, incorporating the probabilistic forecasts and user preference vectors. A retail electricity market with day-ahead (DA) and real-time (RT) markets is employed for leveraging imperfect forecasts. Finally, we design a distributed coordinating algorithm - Asynchronous Scheduling and Iterative Pricing for PV power-sharing among multiple prosumers based on the single-house scheduling model. Numerical simulations based on realistic loads and PV generation data validated the two-stage stochastic programming model's economic superiority and the distributed PV power-sharing approach compared with the rule-based dispatching and selfish scheduling strategies. We concluded that 1) the modeling of load/PV forecast uncertainties is valuable than averaging or ignoring them, 2) the two-stage stochastic programming model and the DA-RT retail electricity market are beneficial for utilizing imperfect forecasts, and 3) coordinating multiple prosumers could benefit each household by sharing PV and battery investments for revenue or trading with local small prosumers for cost reductions.
In the current era, electricity demand has skyrocketed. Power grids have to face a lot of uneven power demand daily. During a certain period in a day, the power demand peaks, making it difficult for the grid to meet the demand. To deal with this problem, an intelligent Home Energy Management (HEM) can be beneficial. Smart HEM systems can schedule loads from peak to low peak hours. Thereby reducing peak load on the grid as well as reducing decreasing the costs incurred by a user. In this paper, we proposed a Deep Reinforcement Learning model with prioritized experience sampling (PQDN-DR) for appropriate demand response, and the problem of load shifting is simulated as a game. We also propose a novel reward system for better convergence of the DRL model to near-optimal strategies and a DR adapted Epsilon Greedy Policy to guide the agent in exploration phase for faster convergence. The proposed system minimizes power demand peak and consumers’ bills simultaneously. The proposed method has successfully reduced the peak load and peak costs in smaller DR environment. The agent reduced costs and overall variance of the load profile for all customers for 24 h in the standard DR environment.
For the last couple of decades, power consumption has been growing exponentially. The traditional power grids are experiencing various challenges such as reliability and sustainability. Due to limited energy resources and budget with increase in load demand, conventional energy management techniques are getting failed. In this case, demand side management (DSM) technique is one of the best solutions ensuring reliable and economical power flow. Demand side management consists of the activities or technologies which are used on demand side to optimize power consumption for achieving desired objectives including energy balancing and cost reduction etc. This chapter reviews and discusses the framework of demand side management on the basis of modes and programs. Demand side management techniques are differentiated on the basis of their implementation and usage, which has not been reported in the previous literature. Residential DSM system is discussed with categorization of loads and constraints. During the organization of the chapter, different optimizing models are also reviewed for the implementation of DSM programs.
We are moving towards a highly distributed service-oriented energy infrastructure where providers and consumers heavily interact with interchangeable roles. Smart meters empower an advanced metering infrastructure which is able to react almost in real time, provide fine-grained energy production or consumption info and adapt its behavior proactively. We focus on the infrastructure itself, the role and architecture of smart meters as well as the security and business implications. Finally we discuss on research directions that need to be followed in order to effectively support the energy networks on the future
Demand response (DR) can be defined as change in electric usage by end-use customers from their normal consumption patterns in response to change in the price of electricity over time. Demand Response also refers to incentive payments designed to induce lower electricity use at times of high wholesale market prices. Time-of-use (TOU) power pricing has been shown to have a significant influence on ensuring a stable and optimal operation of a power system. This paper presents a novel algorithm for finding an optimum time-of-use electricity pricing in monopoly utility markets; definitions and the relations between supply and demand as well as different cost components are also presented. Further, the optimal pricing strategy is developed to maximize the benefit of society while implementing a demand response strategy. Finally, the effect of demand response in electricity prices is demonstrated using a simulated case study.
Demand response (DR) programs encourage end-use customers to alter their power consumption in response to DR events such as change in real-time electricity prices. Facilitating household participation in DR programs is essential as the residential sector accounts for a sizable portion of the total energy consumed. However, manually tracking energy prices and deciding on how to schedule home appliances can be a challenge for residential consumers who are accustomed to fixed price electricity taris. In this work, we present Yupik, a system that helps users respond to real-time electricity prices while being sensitive to their context and lifestyle. Yupik combines sensing, analytics, and optimization to generate appliance usage schedules that may be used by households to minimize their energy bill as well as potential lifestyle disruptions. Yupik uses jPlugs, appliance level energy metering devices, to continuously monitor the power usage by various home appliances. The consumption patterns as well as data from external sources are analyzed using data mining algorithms to infer user's preferred usage profile. Using the preferred profile as a reference, Yupik's optimization engine generates multiple usage plans that attempt to minimize energy and inconvenience costs. Some of Yupik's capabilities are demonstrated with the help of preliminary data collected from a home that was instrumented with jPlugs to monitor the power usage of a few devices.
We are moving towards a highly distributed service-oriented energy infrastructure where providers and consumers heav-ily interact with interchangeable roles. Smart meters empower an advanced metering infrastructure which is able to react almost in real time, provide fine-grained energy production or consumption info and adapt its behavior proactively. We focus on the infrastruc-ture itself, the role and architecture of smart meters as well as the security and business implications. Finally we discuss on research directions that need to be followed in order to effectively support the energy networks on the future.
This paper proposes a Real-Time Pricing (RTP)-based power scheduling scheme as demand response for residential power usage. In this scheme, the Energy Management Controller (EMC) in each home and the service provider form a Stackelberg game, in which the EMC who schedules appliances' operation plays the follower level game, and the provider who sets the real-time prices according to current power usage profile plays the leader level game. The sequential equilibrium is obtained through the information exchange between them. Simulation results indicate that our scheme can not only save money for consumers, but also reduce peak load and the variance between demand and supply, while avoiding the "rebound" peak problem.
Every aspect of human life is crucially determined by the result of decisions. Whereas private decisions may be based on emotions or personal taste, the complex professional environment of the 21st century requires a decision process which can be formalized and validated independently from the involved individuals. Therefore, a quantitative formulation of all factors influencing a decision and also of the result of the decision process is sought.
Suppliers in competitive electricity markets regularly respond to prices that change hour by hour or even more frequently, but most consumers respond to price changes on a very different time scale, i.e., they observe and respond to changes in price as reflected on their monthly bills. In this paper, we examine mixed complementarity programming models of equilibrium that can bridge the speed of response gap between suppliers and consumers yet adhere to the principle of marginal cost pricing of electricity. We develop a computable equilibrium model to estimate ex ante time-of-use (TOU) prices for a retail electricity market. It is intended that the proposed models would be useful 1) for jurisdictions (e.g., Ontario) where consumers' prices are regulated, but suppliers offer into a competitive market, 2) for forecasting forward prices in unregulated markets, and 3) in evaluation and welfare analysis of the policies regarding regulated TOU pricing compared to regulated single pricing
Third edition. "Since publication of the second edition, there have been extensive changes in the algorithms, methods, and assumptions in energy management systems that analyze and control power generation. This edition is updated to acquaint electrical engineering students and professionals with current power generation systems. Algorithms and methods for solving integrated economic, network, and generating system analysis are provided. Also included are the state-of-the-art topics undergoing evolutionary change, including market simulation, multiple market analysis, multiple interchange contract analysis, contract and market bidding, and asset valuation under various portfolio combinations"-- "Online video course with powerpoint slides for each chapter at www.cusp.umn.edu; site also contains links to important research reports, an entire set of student programs in MATLAB, and sets of power system sample data sets for use in student exercises"-- Preface to the third edition -- Preface to the second edition -- Preface to the first edition -- Acknowledgment -- Introduction -- Industrial organization, managerial economics, and finance -- Economic dispatch of thermal units and methods of solution -- Unit commitment -- Generation with limited energy supply --Transmission system effects -- Power system security -- Optimal power flow -- Introduction to state estimation in power systems -- Control of generation -- Interchange, pooling, brokers, and auctions -- Short-term demand forecasting -- Index.
Systems and methods are described that allow a Programmable Logic Controller (PLC) to receive Demand Response (DR) data and process the data in a PLC Function Block (FB). Embodiments provide a PLC demand response FB that solicits DR data and a demand response load manager FB that compares the DR data with predetermined demand constraints corresponding to electrical equipment. The demand constraints provide energy consumption strategies for buildings and factories.
Elements of Pure Economics was one of the most influential works in the history of economics, and the single most important contribution to the marginal revolution. Walras' theory of general equilibrium remains one of the cornerstones of economic theory more than 100 years after it was first published.
Typical user demands of electricity vary throughout the day, which increases the cost to utility companies and decreases the stability of the power system. Time-of-use (TOU) pricing has been proposed as a demand-side management (DSM) method to influence user demands. In this paper, we describe a new approach of optimal TOU pricing strategy based on game theory (GT-TOU). We propose models for costs due to the fluctuating user demands to the utility companies, as well as the user satisfaction measurement because of the difference between the demand and actual load. We design utility functions for the company and the user, and obtain the Nash equilibrium using backward induction and iterative methods. Numerical example shows that our method is effective in leveling the user demand by setting optimal TOU prices, in potentially increasing the profit of the utility companies and ensuring overall user benefit.
We propose a framework for demand response in smart grids that integrates renewable distributed generators (DGs). In this model, some users have DGs and can generate part of their electricity. They can also sell extra generation to the utility company. The goal is to optimize the load schedule of users to minimize the utility company's cost and user payments, while considering user satisfaction. We employ a parallel autonomous optimization scheme, where each user requires only the knowledge of the aggregated load of other users, instead of the load profiles of individual users. All the users can execute distributed optimization simultaneously. The distributed optimization is coordinated through a soft constraint on changes of load schedules between iterations. Numerical examples show that our method can significantly reduce the peak-hour load and costs to the utility and users. Since the autonomous user optimization is executed in parallel, our method also significantly decreases the computation time and communication costs.
In the future smart grid, both users and power companies can potentially benefit from the economical and environmental advantages of smart pricing methods to more effectively reflect the fluctuations of the wholesale price into the customer side. In addition, smart pricing can be used to seek social benefits and to implement social objectives. To achieve social objectives, the utility company may need to collect various information about users and their energy consumption behavior, which can be challenging. In this paper, we propose an efficient pricing method to tackle this problem. We assume that each user is equipped with an energy consumption controller (ECC) as part of its smart meter. All smart meters are connected to not only the power grid but also a communication infrastructure. This allows two-way communication among smart meters and the utility company. We analytically model each user's preferences and energy consumption patterns in form of a utility function. Based on this model, we propose a Vickrey-Clarke-Groves (VCG) mechanism which aims to maximize the social welfare, i.e., the aggregate utility functions of all users minus the total energy cost. Our design requires that each user provides some information about its energy demand. In return, the energy provider will determine each user's electricity bill payment. Finally, we verify some important properties of our proposed VCG mechanism for demand side management such as efficiency, user truthfulness, and nonnegative transfer. Simulation results confirm that the proposed pricing method can benefit both users and utility companies.
This report describes the results of a research project to develop and evaluate the performance of new Automated Demand Response (Auto-DR) hardware and software technology in large facilities. Demand Response (DR) is a set of activities to reduce or shift electricity use to improve electric grid reliability, manage electricity costs, and ensure that customers receive signals that encourage load reduction during times when the electric grid is near its capacity. The two main drivers for widespread demand responsiveness are the prevention of future electricity crises and the reduction of electricity prices. Additional goals for price responsiveness include equity through cost of service pricing, and customer control of electricity usage and bills. The technology developed and evaluated in this report could be used to support numerous forms of DR programs and tariffs. For the purpose of this report, we have defined three levels of Demand Response automation. Manual Demand Response involves manually turning off lights or equipment; this can be a labor-intensive approach. Semi-Automated Response involves the use of building energy management control systems for load shedding, where a preprogrammed load shedding strategy is initiated by facilities staff. Fully-Automated Demand Response is initiated at a building or facility through receipt of an external communications signal--facility staff set up a pre-programmed load shedding strategy which is automatically initiated by the system without the need for human intervention. We have defined this approach to be Auto-DR. An important concept in Auto-DR is that a facility manager is able to ''opt out'' or ''override'' an individual DR event if it occurs at a time when the reduction in end-use services is not desirable. This project sought to improve the feasibility and nature of Auto-DR strategies in large facilities. The research focused on technology development, testing, characterization, and evaluation relating to Auto-DR. This evaluation also included the related decisionmaking perspectives of the facility owners and managers. Another goal of this project was to develop and test a real-time signal for automated demand response that provided a common communication infrastructure for diverse facilities. The six facilities recruited for this project were selected from the facilities that received CEC funds for new DR technology during California's 2000-2001 electricity crises (AB970 and SB-5X).
Finding the electrical conductivity of tissue is highly important for understanding the tissue's structure and functioning. However, the inverse problem of inferring spatial conductivity from data is highly ill-posed and computationally intensive. In this paper, we propose a novel method to solve the inverse problem of inferring tissue conductivity from a set of transmembrane potential and stimuli measurements made by microelectrode arrays (MEA). We first formalize the discrete forward model of transmembrane potential propagation, based on a reaction-diffusion model with an anisotropic inhomogeneous electrical conductivity-tensor field. Then, we propose a novel parallel optimization algorithm for solving the complex inverse problem of estimating the electrical conductivity-tensor field. Specifically, we propose a single-step approximation with a parallel block-relaxation optimization routine that simplifies the joint tensor field estimation problem into a set of computationally tractable subproblems, allowing the use of efficient standard optimization tools. Finally, using numerical examples of several electrical conductivity field topologies and noise levels, we analyze the performance of our algorithm, and discuss its application to real measurements obtained from smooth-muscle cardiac tissue, using data collected with a high-resolution MEA system.
Demand side management will be a key component of future smart grid that can help reduce peak load and adapt elastic demand to fluctuating generations. In this paper, we consider households that operate different appliances including PHEVs and batteries and propose a demand response approach based on utility maximization. Each appliance provides a certain benefit depending on the pattern or volume of power it consumes. Each household wishes to optimally schedule its power consumption so as to maximize its individual net benefit subject to various consumption and power flow constraints. We show that there exist time-varying prices that can align individual optimality with social optimality, i.e., under such prices, when the households selfishly optimize their own benefits, they automatically also maximize the social welfare. The utility company can thus use dynamic pricing to coordinate demand responses to the benefit of the overall system. We propose a distributed algorithm for the utility company and the customers to jointly compute this optimal prices and demand schedules. Finally, we present simulation results that illustrate several interesting properties of the proposed scheme.
Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges.
In this paper, we consider a smart power infrastructure, where several subscribers share a common energy source. Each subscriber is equipped with an energy consumption controller (ECC) unit as part of its smart meter. Each smart meter is connected to not only the power grid but also a communication infrastructure such as a local area network. This allows two-way communication among smart meters. Considering the importance of energy pricing as an essential tool to develop efficient demand side management strategies, we propose a novel real-time pricing algorithm for the future smart grid. We focus on the interactions between the smart meters and the energy provider through the exchange of control messages which contain subscribers' energy consumption and the real-time price information. First, we analytically model the subscribers' preferences and their energy consumption patterns in form of carefully selected utility functions based on concepts from microeconomics. Second, we propose a distributed algorithm which automatically manages the interactions among the ECC units at the smart meters and the energy provider. The algorithm finds the optimal energy consumption levels for each subscriber to maximize the aggregate utility of all subscribers in the system in a fair and efficient fashion. Finally, we show that the energy provider can encourage some desirable consumption patterns among the subscribers by means of the proposed real-time pricing interactions. Simulation results confirm that the proposed distributed algorithm can potentially benefit both subscribers and the energy provider.
In this paper, we study Demand Response (DR) problematics for different levels of information sharing in a smart grid. We propose a dynamic pricing scheme incentivizing consumers to achieve an aggregate load profile suitable for utilities, and study how close they can get to an ideal flat profile depending on how much information they share. When customers can share all their load profiles, we provide a distributed algorithm, set up as a cooperative game between consumers, which significantly reduces the total cost and peak-to-average ratio (PAR) of the system. In the absence of full information sharing (for reasons of privacy), when users have only access to the instantaneous total load on the grid, we provide distributed stochastic strategies that successfully exploit this information to improve the overall load profile. Simulation results confirm that these solutions efficiently benefit from information sharing within the grid and reduce both the total cost and PAR.
We describe algorithmic enhancements to a decision-support tool that residential consumers can utilize to optimize their acquisition of electrical energy services. The decision-support tool optimizes energy services provision by enabling end users to first assign values to desired energy services, and then scheduling their available distributed energy resources (DER) to maximize net benefits. We chose particle swarm optimization (PSO) to solve the corresponding optimization problem because of its straightforward implementation and demonstrated ability to generate near-optimal schedules within manageable computation times. We improve the basic formulation of cooperative PSO by introducing stochastic repulsion among the particles. The improved DER schedules are then used to investigate the potential consumer value added by coordinated DER scheduling. This is computed by comparing the end-user costs obtained with the enhanced algorithm simultaneously scheduling all DER, against the costs when each DER schedule is solved separately. This comparison enables the end users to determine whether their mix of energy service needs, available DER and electricity tariff arrangements might warrant solving the more complex coordinated scheduling problem, or instead, decomposing the problem into multiple simpler optimizations.
In this paper, we consider deployment of energy consumption scheduling (ECS) devices in smart meters for autonomous demand side management within a neighborhood, where several buildings share an energy source. The ECS devices are assumed to be built inside smart meters and to be connected to not only the power grid, but also to a local area network which is essential for handling two-way communications in a smart grid infrastructure. They interact automatically by running a distributed algorithm to find the optimal energy consumption schedule for each subscriber, with an aim at reducing the total energy cost as well as the peak-to-average-ratio (PAR) in load demand in the system. Incentives are also provided for the subscribers to actually use the ECS devices via a novel pricing model, derived from a game-theoretic analysis. Simulation results confirm that our proposed distributed algorithm significantly reduces the PAR and the total cost in the system.
We describe the results of a time-of-use (TOU) rate option experiment which demonstrates that offering a TOU option can be profitable to a utility. The option's profitability is attributable to: (1) smart meters; (2) large price differentials between TOU periods that minimize the adverse selection problem of free-riders; and (3) the success of marketing efforts that enhance customer acceptance. This finding refutes the common belief that rate options are necessarily unprofitable to a utility and unwanted by small users. We explore the significance and relevance of our findings in the emerging world or retail access. TOU rate options will continue to be useful to unregulated energy suppliers, regulated wires companies and electricity consumers.
Empirical evidence concerning demand response (DR) resources is needed in order to establish baseline conditions, develop standardized methods to assess DR availability and performance, and to build confidence among policymakers, utilities, system operators, and stakeholders that DR resources do offer a viable, cost-effective alternative to supply-side investments. This paper summarizes the existing contribution of DR resources in U.S. electric power markets. In 2008, customers enrolled in existing wholesale and retail DR programs were capable of providing ∼38,000 MW of potential peak load reductions in the United States. Participants in organized wholesale market DR programs, though, have historically overestimated their likely performance during declared curtailments events, but appear to be getting better as they and their agents gain experience. In places with less developed organized wholesale market DR programs, utilities are learning how to create more flexible DR resources by adapting legacy load management programs to fit into existing wholesale market constructs. Overall, the development of open and organized wholesale markets coupled with direct policy support by the Federal Energy Regulatory Commission has facilitated new entry by curtailment service providers, which has likely expanded the demand response industry and led to product and service innovation.
This paper presents a design and evaluates the performance of a power consumption scheduler in smart grid homes, aiming at reducing the peak load in individual homes as well as in the system-wide power transmission network. Following the task model consist of actuation time, operation length, deadline, and a consumption profile, the scheduler copies or maps the profile according to the task type, which can be either preemptive or nonpreemptive. The proposed scheme expands the search space recursively to traverse all the feasible allocations for a task set. A pilot implementation of this scheduling method reduces the peak load by up to 23.1% for the given task set. The execution time greatly depends on the search space of a preemptive task, as its time complexity is estimated to be O (MNnp · (MM/2)Np), where M, Nnp, and Np are the number of time slots, preemptive tasks, and nonpreemptive tasks, respectively. However, it can not only be reduced almost to 2% but also made stable with a basic constraint processing mechanism which prunes a search branch when the partial peak value already exceeds the current best.
Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, recent studies have revealed that the lack of knowledge among users about how to respond to time-varying prices as well as the lack of effective building automation systems are two major barriers for fully utilizing the potential benefits of real-time pricing tariffs. We tackle these problems by proposing an
optimal
and
automatic
residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the
electricity payment
and minimizing the
waiting time
for the operation of each appliance in household in presence of a real-time pricing tariff
combined
with
inclining block rates
. Our design requires minimum effort from the users and is based on simple
linear programming
computations. Moreover, we argue that any residential load control strategy in real-time electricity pricing environments requires
price prediction
capabilities. This is particularly true if the utility companies provide price information only one or two hours ahead of time. By applying a simple and efficient
weighted average price prediction
filter to the actual hourly-based price values used by the Illinois Power Company from January 2007 to December 2009, we obtain the optimal choices of the
coefficients
for each day of the week to be used by the price predictor filter. Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting
peak-to-average ratio
in load demand for various load scenarios. Therefore, the deployment of the proposed optimal energy consumption scheduling schemes is beneficial for both end users and utility companies.
The well-studied 0/1 Knapsack and Subset-Sum Problem are maximization problems that have an equivalent minimization version. While exact algorithms for one of these two versions also yield an exact solution for the other version, this does not apply to ε-approximate algorithms. We present several ε-approximate Greedy Algorithms for the minimization version of the 0/1 Knapsack and the Subset-Sum Problem, that are also ε-approximate for the respective maximization version.
In PJM, 15% of electric generation capacity ran less than 96 hours, 1.1% of the time, over 2006. If retail prices reflected hourly wholesale market prices, customers would shift consumption away from peak hours and installed capacity could drop. We use PJM data to estimate consumer and producer savings from a change toward real-time pricing (RTP) or time-of-use (TOU) pricing. Surprisingly, neither RTP nor TOU has much effect on average price under plausible short-term consumer responses. Consumer plus producer surplus rises 2.8%-4.4% with RTP and 0.6%-1.0% with TOU. Peak capacity savings are seven times larger with RTP. Peak load drops by 10.4%-17.7% with RTP and only 1.1%-2.4% with TOU. Half of all possible customer savings from load shifting are obtained by shifting only 1.7% of all MWh to another time of day, indicating that only the largest customers need be responsive to get the majority of the short-run savings.
Demand-side management (DSM) is the planning and implementation of those electric utility activities designed to influence customer uses of electricity in ways that will produce desired changes in the utility's load shape. While the objective of any DSM activity is to produce a load-shape change, the art of successful implementation and the ultimate success of the program rests within the balancing of utility and customer needs. This paper describes demand-side management for electric utilities and discusses the evolution of this concept for load management, strategic conservation, and marketing.
Synergy potentials of smart appliances University of Bonn, D2.3 of WP 2 of the Smart-A Project
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Demand response enabled appliances and home energy management systems General Electric Consumer and Industrial presentation to NREL
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KV series PLC, Online Product Catalog
- Keyenceamerica
Lo âPLCfunction block for automated demand response integration,â InternationalClassification G05B 15/02 Patent
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“Demand response enabled appliances and home energy management systems,”
- D Najewicz
“Synergy potentials of smart appliances,”
- R Stamminger
Programmable Logic Controllers: The Complete Guide to the Technology
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