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An Efficient Home Energy Optimization by Using Meta-heuristic Techniques While Incorporating Game-theoretic Approach for Real-time Coordination Among Home Appliances
Abstract
For the optimization of home energy consumption, we proposed a system model based on scheduling techniques and also, incorporated real-time coordination among household appliances by using game theory (GT). Main objective of scheduling techniques is to decrease the electricity cost of consumers by efficiently managing the home energy consumption on the basis of real-time pricing (RTP) signal whereas, coordination is implemented among household appliances in order to increase the user comfort by decreasing the appliances delay. Scheduling techniques: cuckoo search algorithm (CSA), earthworm algorithm (EWA), bat algorithm (BA) and a proposed hybrid scheme (HCEO), originated from CSA and EWA, are implemented and results demonstrate that proposed hybrid scheme has reduced the total electricity cost by 49.09% as compared to unscheduled case and it outperformed other scheduling techniques in terms of cost reduction. Performance of scheduling techniques before and after coordination is evaluated and a comparison is performed with each other. Results indicate that a trade-off exists between electricity cost and appliances delay.
... As a competitive choice, the meta-heuristic algorithm is not only insensitive to initial conditions, but also does not require the continuity of the solution space. Compared with traditional gradient-based optimization methods, these methods can be used to solve continuous, multi-modal, non-convex, multidimensional optimization problems with large differences in properties, such as [9] [10]. Most of these algorithms imitate human and natural behavior; they will automatically start to find the optimal solution after initialization, and do not involve complicated calculations. ...
... In [10], the authors propose two popular topologies for HAPF for harmonic compensation in industrial power systems, as shown in Fig. 1. In Fig. 1, and represent the transmission system resistance and inductive reactance in ohms at harmonic 'H', respectively; and denote load resistance and inductive reactance in ohms at harmonic 'H' respectively; ' ' means controllable feedback gain of HAPF on ohms; and indicate fundamental inductive and capacitive reactance in ohms of the passive filter; and represent the root mean square (RMS) value of supply current and load current in amperes at harmonic 'H', respectively; and denote the RMS value of supply voltage and load voltage (line-to-neutral) at harmonic 'H', respectively. ...
Hybrid power active filter (HAPF) is an important device to suppress the harmonics of the power system. In HAPF, the parameters estimation has a great impact on ensuring the power quality in the power system. Aiming at the problem of minimizing the harmonic pollution (HP) in the power system, this paper proposes a new technology namely IEDA for parameter optimization of hybrid power active filters, which is an improved dragonfly algorithm (DA) with higher exploitation capability. DA is a global search algorithm with sufficient ability to avoid falling into local optimization, however, DA performs poorly for local search. In the IEDA, we adopt a strategy of division of labor to divide particles into exploitation population and exploration population. In the exploitation population, we introduce the information exchange mechanism of the differential evolution (DE) and set up an exemplar pool to enhance its exploitation capability. In the exploration population, we use the global search ability of the DA to prevent particles from falling into a local optimum. Through the division of labor between exploitation population and exploration population, the problems of low accuracy and slow convergence of DA are effectively solved. Experimental results show that the algorithm has greatly improved accuracy and reliability compared with seven well-established algorithms.
... However, user comfort is not considered. Similar work is also done in Reference [39]. In short, one basic problem in establishing the schedule of the appliances lies in the uncertainty of human behavior. ...
The feature of bidirectional communication in a smart grid involves the interaction between consumer and utility for optimizing the energy consumption of the users. For optimal management of the energy at the end user, several demand side management techniques are implemented. This work proposes a home energy management system, where consumption of household appliances is optimized using a hybrid technique. This technique is developed from cuckoo search algorithm and earthworm algorithm. However, there is a problem in such home energy management systems, that is, an uncertain behavior of the user that can lead to force start or stop of an appliance, deteriorating the purpose of scheduling of appliances. In order to solve this issue, coordination among appliances for rescheduling is incorporated in home energy management system using game theory. The appliances of the home are categorized in three different groups and their electricity cost is computed through the real-time pricing signals. Optimization schemes are implemented and their performance is scrutinized with and without coordination among the appliances. Simulation outcomes display that our proposed technique has minimized the total electricity cost by 50.6% as compared to unscheduled cost. Moreover, coordination among appliances has helped in increasing the user comfort by reducing the waiting time of appliances. The Shapley value has outperformed the Nash equilibrium and zero sum by achieving the maximum reduction in waiting time of appliances.
Supply-and-demand-side resource management and demand-side load management (DSLM) are important techniques for addressing power system uncertainties. Demand-side load management allows the load profile to be reshaped to reduce the peak demand and overall cost. Many demand-side load management problems have been solved using different demand response programmesprograms as well as conventional numeric and metaheuristic methods. However, it can be applied only toonly to a limited number of devices of certain types. Of the six direct load control demand response techniques for demand-side load management, the performance of the day-ahead load-shifting and flexible load curve DSLM techniques are compared in this study. These techniques can be used for a larger number of devices of more types. The demand-side load management problem is formulated as a minimization problem to achieve peak demand reduction and cost minimization. Novel metaheuristic optimizers are used to perform demand-side load management, and comparative analysis is conducted for the cost and peak load reduction. The simulation results are verified using the fmincon function of MATLAB. The simulation results indicate that the aforementioned algorithms can be applied to a larger number of devices of more types to achieve considerable savings by minimizing the cost and peak load demand. Moreover, the load-shifting demand-side load management technique is more beneficial from the system operator’s perspective than from the customer’s perspective. In contrast, the flexible load curve demand-side load management technique is more beneficial from the customer’s perspective.
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.
IIn this paper, we propose a home energy management system which employs load shifting strategy of demand side management to optimize the energy consumption patterns of a smart home. It aims to manage the load demand in an efficient way to minimize electricity cost and peak to average ratio while maintaining user comfort through coordination among home appliances. In order to meet the load demand of electricity consumers, we schedule the load in day-ahead and real-time basis. We propose a fitness criterion for proposed hybrid technique which helps in balancing the load during On-peak and Off-peak hours. Moreover, for real-time rescheduling, we present the concept of coordination among home appliances. This helps the scheduler to optimally decide the ON/OFF status of appliances in order to reduce the waiting time of appliance. For this purpose, we formulate our realtime rescheduling problem as knapsack problem and solve it through dynamic programming. This study also evaluates the behavior of the proposed technique for three pricing schemes including: time of use, real-time pricing and critical peak pricing. Simulation results illustrate the significance of the proposed optimization technique with 95% confidence interval.
This paper proposes a distributed optimization algorithm for scheduling the energy consumption of multiple smart homes with distributed energy resources. In the proposed approach, the centralized optimization problem for home energy management is decomposed into a two-level optimization problem, corresponding to the local home energy management system (LHEMS) at the first level and the global home energy management system (GHEMS) at the second level. The controllable household appliances (e.g., air conditioner, washing machine) are scheduled in the LHEMS within consumer’s preferred appliance scheduling and comfort level while the energy storage system (ESS) and power trading between households are scheduled in the GHEMS. In the simulation study, the proposed distributed algorithm shows almost equivalent performance to the centralized algorithm in terms of the electricity cost and the consumer’s comfort level. The impact of different network topologies on the proposed algorithm is also analyzed, and the result provides insight into the selection of the optimal network configuration in view of the consumer’s electricity cost saving.
In this paper a new approach which is based on a collaborative system of MicroGrids (MG’s), is proposed to enable household appliance scheduling. To achieve this, appliances are categorized into flexible and non-flexible Deferrable Loads (DL’s), according to their electrical components. We propose a dynamic scheduling algorithm where users can systematically manage the operation of their electric appliances. The main challenge is to develop a flattening function calculus (reshaping) for both flexible and non-flexible DL’s. In addition, implementation of the proposed algorithm would require dynamically analyzing two successive multi-objective optimization (MOO) problems. The first targets the activation schedule of non-flexible DL’s and the second deals with the power profiles of flexible DL’s. The MOO problems are resolved by using a fast and elitist multi-objective genetic algorithm (NSGA-II). Finally, in order to show the efficiency of the proposed approach, a case study of a collaborative system that consists of 40 MG’s registered in the load curve for the flattening program has been developed. The results verify that the load curve can indeed become very flat by applying the proposed scheduling approach.
Energy management means to optimize one of the most complex and important technical creations that we know: the energy system. While there is plenty of experience in optimizing energy generation and distribution, it is the demand side that receives increasing attention by research and industry. Demand Side Management (DSM) is a portfolio of measures to improve the energy system at the side of consumption. It ranges from improving energy efficiency by using better materials, over smart energy tariffs with incentives for certain consumption patterns, up to sophisticated real-time control of distributed energy resources. This paper gives an overview and a taxonomy for DSM, analyzes the various types of DSM, and gives an outlook on the latest demonstration projects in this domain.
This paper details a proposed demand response (DR) application to optimize the operation of appliances in an indeterminate environment in a home energy management system (HEMS). An indeterminate environment results from forecasted errors of electricity prices and system loads, so a probabilistic analysis of the system performance is of significant interest. Herein, a chance constrained, optimization-based model is formulated to accommodate these uncertainties. The resulting DR application can be easily embedded in resource limited electric devices. To reduce the computational cost, both improved particle swarm optimization (PSO) and a two-point estimate method are presented to solve the chance constrained problem. The improved PSO is used to provide the optimum solution, while the probabilistic assessment of uncertainties is estimated using a two-point estimate method. Numerical comparisons were made to justify the effectiveness of the method. The simulated results obtained using the models indicate that the proposed method can significantly reduce the computational burden while maintaining a high level of accuracy.
In this paper, we propose a robust approach for scheduling of smart appliances and electrical energy storages (EESs) in active apartments with the aim of reducing both the electricity bill and the CO2 emissions. The proposed robust formulation takes the user behavior uncertainty into account so that the optimal appliances schedule is less sensitive to unpredictable changes in user preferences. The user behavior uncertainty is modeled as uncertainty in the cost function coefficients. In order to reduce the level of conservativeness of the robust solution, we introduce a parameter allowing to achieve a trade-off between the price of robustness and the protection against uncertainty. Mathematically, the robust scheduling problem is posed as a multi-objective Mixed Integer Linear Programming (MILP), which is solved by using standard algorithms. The numerical results show effectiveness of the proposed approach to increase both the electricity bill and CO2 emissions savings, in the presence of user behavior uncertainties. Mathematical insights into the robust formulation are illustrated and the sensitivity of the optimum cost in the presence of uncertainties is investigated. Although home appliances and EESs are considered in this work, we point out that the proposed scheduling framework is generally applicable to many use cases, e.g., charging and discharging of electrical vehicles in an effective way. In addition, it is applicable to various scenarios considering different uncertainty sources, different storage technologies and generic programmable electrical loads, as well as different optimization criteria.
Severe peak rebounds are likely in absence of a system-wide coordination among customers participating in demand response programs. This paper aims to establish a decentralized system-wide framework to coordinate demand response of residential customers in a smart grid. The objective of the framework is to modify system load profile provided that customers' payments are minimized, and their comfort and privacy are preserved. Home load management (HLM) modules, embedded in customers' smart meters are autonomous agents of the framework. The energy service provider iteratively exchanges load information with HLM modules in the hope of achieving his desired load profile. In each iteration, the service provider announces system load profile to HLM modules. The modules, keeping in mind their own financial and comfort constraints, nonsequentially send back load reschedule proposals to modify system load profile. The received proposals are judged whether they improve system load profile or not. HLM modules with accepted proposals apply their proposed schedules. The modified system load profile is then released, and HLM modules' new proposals are gathered and judged. This procedure is repeated to the point at which no further improvement in the system load profile can be experienced. Performance of the framework is shown by applying it to a system with 50 customers.
In a smart community infrastructure that consists of multiple smart homes, smart controllers schedule various home appliances to balance energy consumption and reduce electricity bills of customers. In this paper, the impact of the smart home scheduling to the electricity market is analyzed with a new smart-home-aware bi-level market model. In this model, the customers schedule home appliances for bill reduction at the community level, whereas aggregators minimize the energy purchasing expense from utilities at the market level, both of which consider the smart home scheduling impacts. A game-theoretic algorithm is proposed to solve this formulation that handles the bidirectional influence between both levels. Comparing with the electricity market without smart home scheduling, our proposed infrastructure balances the energy load through reducing the peak-to-average ratio by up to 35.9%, whereas the average customer bill is reduced by up to 34.3%.
Due to the large amount of energy consumed in buildings, building energy savings has attracted more and more attention recently. The total energy consumed during building operations is determined by the building energy efficiency and the total demand. On the one hand, though most existing studies focus on improving building energy efficiency, there are limits. On the other hand, the demand grows fast and without limit. Therefore it is important to coordinate the supply and demand in buildings. We consider this important problem in this paper and make the following major contributions. First, the concept of average price of electricity (APE) is defined to measure the average generation cost of electricity using multiple devices. Second, a comfort model of occupant is developed to capture the tradeoff between thermal comfort and cost. Human building interaction allows the user to adjust their temperature set ranges according to the APE in real time. Third, an iterative solution method is developed to solve the supply demand coordination optimization problem. Numerical examples show that significant energy saving is possible through exploring the soft comfort requirement of the occupants, and the iterative method achieves a solution which is close to that of the centralized method, but in a much faster way. We hope this work brings insight to building energy saving in general.
Facilitated by advanced information and communication technologies (ICT) infrastructure and optimization techniques, smart grid has the potential to bring significant benefits to the energy consumption management. This paper presents a game theoretic consumption scheduling framework based on the use of mixed integer programming (MIP) to schedule consumption plan for residential consumers. In particular, the optimization framework incorporates integration of locally generated renewable energy in order to minimize dependency on conventional energy and the consumption cost. The game theoretic model is designed to coordinatively manage the scheduling of appliances of consumers. The Nash equilibrium of the game exists and the scheduling optimization converges to an equilibrium where all consumers can benefit from participating in. Simulation results are presented to demonstrate the proposed approach and the benefits of home demand management.
We present a novel energy management system for residential demand response. The algorithm, named CAES, reduces residential energy costs and smooths energy usage. CAES is an online learning application that implicitly estimates the impact of future energy prices and of consumer decisions on long term costs and schedules residential device usage. CAES models both energy prices and residential device usage as Markov, but does not assume knowledge of the structure or transition probabilities of these Markov chains. CAES learns continuously and adapts to individual consumer preferences and pricing modifications over time. In numerical simulations CAES reduced average end-user financial costs from 16% to 40% with respect to a price-unaware energy allocation.
Residential Demand Response Using Reinforcement Learning
- D Neill
- M Levorato
- A Goldsmith
- U Mitra