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Pigeon Inspired Optimization and Bacterial Foraging Optimization for Home Energy Management
Abstract
In this paper, we are dealing with Home Energy Management System (HEMS) using Bacterial Foraging Optimization (BFO) and Pigeon Inspired Optimization (PIO) techniques in a single home. Performance of Both techniques is evaluated through simulations in term of reduction in electricity cost, Peak to Average Ratio (PAR) by scheduling smart appliances. We have used Critical Peak Pricing (CPP) as a pricing signal and we have gained electricity cost reduction upto 40%.
In this paper, a modified version of the Multi-objective Grey Wolf Optimizer (MGWO), known as linked-based GWO (LMGWO), is proposed for the Appliances Energy Scheduling Problem (AESP). The proposed LMGWO is utilized by combining the MGWO searching mechanism with a novel strategy, called neighbourhood selection strategy, to improve local exploitation capabilities. AESP is a problem that can be tackled by searching for the best appliances schedule according to a set of constraints and a dynamic pricing scheme(s) utilized for optimizing energy consumed at a particular period. Three objectives are considered to handle AESP: improving user comfort while reducing electricity bills and maintaining power systems’ performance. Therefore, AESP is modelled as a multi-objective optimization problem to handle all objectives simultaneously. In the evaluation results, the LMGWO is tested using a new dataset containing 30 power consumption scenarios with up to 36 appliances. For comparative purposes, the same linked-based neighbourhood selection strategy is utilized with other three optimization algorithms, including particle swarm optimization, salp swarm optimization, and wind-driven algorithm. The performance of the modified versions is compared with each other and that of the original versions to show their improvements. Furthermore, the proposed LMGWO is compared with eight state-of-the-art methods using their recommended datasets to show the viability of the proposed LMGWO. Interestingly, the proposed LMGWO is able to outperform the compared methods in almost all produced results.
The residential load sector plays a vital role in terms of its impact on overall power balance, stability, and efficient power management. However, the load dynamics of the energy demand of residential users are always nonlinear, uncontrollable, and inelastic concerning power grid regulation and management. The integration of distributed generations (DGs) and advancement of information and communication technology (ICT) even though handles the related issues and challenges up to some extent, till the flexibility, energy management and scheduling with better planning are necessary for the residential sector to achieve better grid stability and efficiency. To address these issues, it is indispensable to analyze the demand-side management (DSM) for the complex residential sector considering various operational constraints, objectives, identifying various factors that affect better planning, scheduling, and management, to project the key features of various approaches and possible future research directions. This review has been done based on the related literature to focus on modeling, optimization methods, major objectives, system operation constraints, dominating factors impacting overall system operation, and possible solutions enhancing residential DSM operation. Gaps in future research and possible prospects have been discussed briefly to give a proper insight into the current implementation of DSM. This extensive review of residential DSM will help all the researchers in this area to innovate better energy management strategies and reduce the effect of system uncertainties, variations, and constraints.
Smart Gird is a technology that has brought many advantages with its evolution. Smart Grid is indispensable as it will lead us towards environmentally sustainable economic growth. Home energy management in Smart Grid is a hot research topic now a days. It aims at reducing the energy cost of users, gaining energy self-reliance and decreasing Greenhouse gas emissions. Renewable energy technologies nowadays are best suitable for off grid services without having to build extensive and complicated infrastructure. With the advent of Smart Grid (SG), the occupants have the opportunity to integrate with renewable energy sources (RESs) and to actively take part in demand side Management (DSM). This review paper is comprehensive study of various optimization techniques and their implementation with respect to electricity cost diminution, load balancing, power consumption and user's comfort maximization etc. for Home Energy Management in Smart Grid. This paper summarizes recent trends of energy usage from hybrid renewable energy integrated sources. It discusses several methodologies and techniques for hybrid renewable energy system optimization.
In this paper, the multi-objective grey wolf optimiser is utilised for the power scheduling problem (PSP). The grey wolf optimiser (GWO) is a recent swarm-based optimisation algorithm tailored for various optimisation problems. PSP is addressed by scheduling home appliances to a certain time horizon to minimise the electricity bill and peak-to-average ratio (PAR) and increase the comfort level of users. The multi-objective function is formalised and utilised in GWO to obtain an optimal schedule. Seven consumption profiles and seven real-time electricity prices with various characteristics are considered to evaluate the proposed multi-objective GWO. The performance of the proposed algorithm is tested against three factors, namely, electricity bill, PAR and user comfort level. The obtained schedule shows that all evaluation factors are optimally timetabled. For a comparative evaluation, the proposed method is firstly compared with the genetic algorithm. The proposed method exhibits and yield better performance than GA under the same consumption profiles. Secondly, the proposed method is compared with 19 state-of-the-art methods by using the recommended consumption profiles of these methods and their evaluation criteria. The proposed method nearly outperforms the compared methods in terms of minimisation of electricity bill and PAR. User comfort level is a criterion proposed in this study and has not been considered previously. It exerts a significant impact on the final schedule.
In recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA), the binary particle swarm optimization (BPSO) algorithm, the bacterial foraging optimization algorithm (BFOA), the wind-driven optimization (WDO) algorithm and our proposed hybrid genetic wind-driven (GWD) algorithm) are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs) and off-peak hours (OPHs) in a real-time pricing (RTP) environment while maximizing user comfort (UC) and minimizing both electricity cost and the peak to average ratio (PAR). Moreover, these algorithms are tested in two scenarios: (i) scheduling the load of a single home and (ii) scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics.
In this work, we propose a Realistic Scheduling Mechanism (RSM) to reduce user frustration and enhance appliance utility by classifying appliances with respective constraints and their time of use effectively. Algorithms are proposed regarding functioning of home appliances. A 24 hour time slot is divided into four logical sub-time slots, each composed of 360 min or 6 h. In these sub-time slots, only desired appliances (with respect to appliance classification) are scheduled to raise appliance utility, restricting power consumption by a dynamically modelled power usage limiter that does not only take the electricity consumer into account but also the electricity supplier. Once appliance, time and power usage limiter modelling is done, we use a nature-inspired heuristic algorithm, Binary Particle Swarm Optimization (BPSO), optimally to form schedules with given constraints representing each sub-time slot. These schedules tend to achieve an equilibrium amongst appliance utility and cost effectiveness. For validation of the proposed RSM, we provide a comparative analysis amongst unscheduled electrical load usage, scheduled directly by BPSO and RSM, reflecting user comfort, which is based upon cost effectiveness and appliance utility.
With the increasing demand for electricity and the advent of smart grids, developed countries are establishing demand side management (DSM) techniques to influence consumption patterns. The use of dynamic pricing strategies has emerged as a powerful DSM tool to optimize the energy consumption pattern of consumers and simultaneously improve the overall efficacy of the energy market. The main objective of the dynamic pricing strategy is to encourage consumers to participate in peak load reduction and obtain respective incentives in return. In this work, a game theory based dynamic pricing strategy is evaluated for Singapore electricity market, with focus on the residential and commercial sector. The proposed pricing model is tested with five load and price datasets to spread across all possible scenarios including weekdays, weekends, public holidays and the highest/lowest demand in the year. Three pricing strategies are evaluated and compared, namely, the half-hourly Real-Time Pricing (RTP), Time-of-Use (TOU) Pricing and Day-Night (DN) Pricing. The results demonstrate that RTP maximizes peak load reduction for the residential sector and commercial sector by 10% and 5%, respectively. Moreover, the profits are increased by 15.5% and 18.7%, respectively, while total load reduction is minimized to ensure a realistic scenario.
In this paper, we comparatively evaluate the performance of home energy management controller which is designed on the basis of heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO) and ant colony optimization (ACO). In this regard, we introduce a generic architecture for demand side management (DSM) which integrates residential area domain with smart area domain via wide area network. In addition, problem formulation is carried via multiple knapsack problem. For energy pricing, combined model of time of use tariff and inclined block rates is used. Simulation results show that all designed models for energy management act significantly to achieve our objections and proven as a cost-effective solution to increase sustainability of smart grid. GA based energy management controller performs more efficiently than BPSO based energy management controller and ACO based energy management controller in terms of electricity bill reduction, peak to average ratio minimization and user comfort level maximization.
Demand-response (DR) is regarded as a promising solution for future power grids. Here we use a Stackelberg game approach, and describe a novel DR model for electricity trading between one utility company and multiple users, which is aimed at balancing supply and demand, as well as smoothing the aggregated load in the system. The interactions between the utility company (leader) and users (followers) are formulated into a 1-leader, N-follower Stackelberg game, where optimization problems are formed for each player to help select the optimal strategy. A pricing function is adopted for regulating real-time prices (RTP), which then act as a coordinator, inducing users to join the game. An iterative algorithm is proposed to derive the Stackelberg equilibrium, through which optimal power generation and power demands are determined for the utility company and users respectively. Numerical results indicate that the proposed method can efficiently reshape users’ demands, including flattening peak demands and filling the vacancy of valley demands, and significantly reduce the mismatch between supply and demand.
This paper studies the power scheduling problem for residential consumers in smart grid. In general, the consumers have two types of electric appliances. The first type of appliances have flexible starting time and work continuously with a fixed power. The second type of appliances work with a flexible power in a predefined working time. The consumers can adjust the starting time of the first type of appliances or reduce the power consumption of the second type of appliances to reduce the payments. However, this will also incur discomfort to the consumers. Assuming the electricity price is announced by the service provider ahead of time, we propose a power scheduling strategy for the residential consumers to achieve a desired trade-off between the payments and the discomfort. The power scheduling is formulated as an optimization problem including integer and continuous variables. An optimal scheduling strategy is obtained by solving the optimization problem. Simulation results demonstrate that the scheduling strategy can achieve a desired tradeoff between the payments and the discomfort.
Demand Side Management (DSM) makes it possible to adjust the load experienced by the power grid while reducing the consumers' bill. Game-theoretic DSM is an appealing decentralized approach for collaboratively scheduling the usage of domestic electrical appliances within a set of households while meeting the users' preferences about the usage time. The drawback of distributed DSM protocols is that they require each user to communicate his/her own energy consumption patterns, which may leak sensitive information regarding private habits.
This paper proposes a distributed Privacy-Friendly DSM system that preserves users' privacy by integrating data aggregation and perturbation techniques: users decide their schedule according to aggregated consumption measurements perturbed by means of Additive White Gaussian Noise (AWGN).
We evaluate the noise power and the number of users required to achieve a given privacy level, quantified by means of the increase of the information entropy of the aggregated energy consumption pattern.
The performance of our proposed DSM system is compared to the one of a benchmark system that does not support privacy preservation in terms of total bill, peak demand and convergence time. Results show that privacy can be improved at the cost of increasing the peak demand and the number of game iterations, whereas the total bill is only marginally incremented.
In this paper, we focus on the problems of load scheduling and power trading in systems with high penetration of renewable energy resources (RERs). We adopt approximate dynamic programming to schedule the operation of different types of appliances including must-run and controllable appliances. We assume that users can sell their excess power generation to other users or to the utility company. Since it is more profitable for users to trade energy with other users locally, users with excess generation compete with each other to sell their respective extra power to their neighbors. A game theoretic approach is adopted to model the interaction between users with excess generation. In our system model, each user aims to obtain a larger share of the market and to maximize its revenue by appropriately selecting its offered price and generation. In addition to yielding a higher revenue, consuming the excess generation locally reduces the reverse power flow, which impacts the stability of the system. Simulation results show that our proposed algorithm reduces the energy expenses of the users. The proposed algorithm also facilitates the utilization of RERs by encouraging users to consume excess generation locally rather than injecting it back into the power grid.
Purpose
– The purpose of this paper is to present a novel swarm intelligence optimizer — pigeon-inspired optimization (PIO) — and describe how this algorithm was applied to solve air robot path planning problems.
Design/methodology/approach
– The formulation of threat resources and objective function in air robot path planning is given. The mathematical model and detailed implementation process of PIO is presented. Comparative experiments with standard differential evolution (DE) algorithm are also conducted.
Findings
– The feasibility, effectiveness and robustness of the proposed PIO algorithm are shown by a series of comparative experiments with standard DE algorithm. The computational results also show that the proposed PIO algorithm can effectively improve the convergence speed, and the superiority of global search is also verified in various cases.
Originality/value
– In this paper, the authors first presented a PIO algorithm. In this newly presented algorithm, map and compass operator model is presented based on magnetic field and sun, while landmark operator model is designed based on landmarks. The authors also applied this newly proposed PIO algorithm for solving air robot path planning problems.
Demand Side Management (DSM) is one of the most important aspects in future smart grids: towards electricity generation cost by minimizing the expensive thermal peak power plants. The DSM greatly affects the individual users' cost as well as the per unit cost. The main objective of this paper is to develop a Generic Demand Side Management (G-DSM) model for residential users to reduce Peak-to-Average Ratio (PAR), total energy cost and Waiting Time of Appliances (WTA) along with fast execution of the proposed algorithm. We propose a system architecture and mathematical formulation for total energy cost minimization, PAR reduction, and WTA. The G-DSM model is based on Genetic Algorithm (GA) for appliances scheduling and considers 20 users having a combination of appliances with different operational characteristics. Simulation results show the effectiveness of G-DSM model both for single and multiple users scenarios.
An integer linear programming based optimization for home demand-side management in smart grid
- Ziming Zhu
- Jie Tang
- Sangarapillai Lambotharan
- Woon Hau Chin
- Zhong Fan
Zhu, Ziming, Jie Tang, Sangarapillai Lambotharan, Woon Hau Chin, and Zhong Fan.
"An integer linear programming based optimization for home demand-side management
in smart grid." In Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES, pp.
1-5. IEEE, 2012.