An Optimal Dispatch Algorithm for Managing Residential Distributed Energy Resources (original) (raw)
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Electric Power Systems Research, 2011
We propose a novel decision-support tool that aims to optimize the provision of residential energy services from the perspective of the end-user. The tool is composed of a novel energy service model and a novel distributed energy resources scheduling algorithm. The proposed model takes into account the time-varying demand and benefit that end-users derive from different services, and assigns the benefit to the energy that realizes the service. The scheduling algorithm determines how distributed energy resources available to the end-users and under their control should be operated so that the net benefit of energy services is maximized based on the energy service models, and their technical characteristics and capabilities. The scheduling is a challenging optimization problem; hence, a heuristic simulation-based approach based around cooperative particle swarm optimization is used. The paper presents a case study where this decision-support tool is used to optimize the provision of desired energy services in a 'smart' home that includes a number of controllable loads, energy storage and photovoltaic generation. (M.A.A. Pedrasa). rent electricity industry into a form that would support sustainable energy service consumption. This may be achieved by formally incorporating the provision of energy services to the operation of the industry; that is, the industry should focus on the provision of energy services instead of just making electricity available at all power receptacles.
Energy, 2020
Today, the fact that consumers are becoming more active in electrical power systems, along with the development in electronic and control devices, makes the design of Home Energy Management Systems (HEMSs) an expedient approach to mitigate their costs. The added costs incurred by consumers are mainly paying for the peak-load demand and the system's operation and maintenance. Thus, developing and utilizing an efficient HEMS would provide an opportunity both to the end-users and system operators to reduce their costs. Accordingly, this paper proposes an effective HEMS design for the self-scheduling of assets of a residential end-user. The suggested model considers the existence of a dynamic pricing scheme such as Real-Time Pricing (RTP), Time-of-Use (TOU), and Inclining Block Rate (IBR), which are effective Demand Response Programs (DRPs) put in place to alleviate the energy bill of consumers and incentivize demand-side participation in power systems. In this respect, the self-scheduling problem is modeled using a stochastic Mixed-Integer Linear Programming (MILP) framework, which allows optimal determination of the status of the home appliances throughout the day, obtaining the global optimal solution with a fast convergence rate. It is noted that the consumer is equipped with self-generation assets through a Photovoltaic (PV) panel and a battery. This system would make the consumers have energy arbitrage and transact energy with the utility grid. Consequently, the proposed model is demonstrated by determining the best operation schedule for different case studies, highlighting the impact each different DRP has on designing and utilizing the HEMS system for best results.
An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources
Traditional power grid and its demand-side management (DSM) techniques are centralized and mainly focus on industrial consumers. The ignorance of residential and commercial sectors in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept of DSM and demand response (DR) via residential sector makes the smart grid (SG) superior over the traditional grid. In this context, this paper proposes an optimized home energy management system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy storage system (ESS) but also incorporates the residential sector into DSM activities. The proposed OHEMS minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of electricity market. First, the constrained optimization problem is mathematically formulated by using multiple knapsack problems, and then solved by using the heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-PSO (HGPO) algorithms. The performance of the proposed scheme and heuristic algorithms is evaluated via MATLAB simulations. Results illustrate that the integration of RES and ESS reduces the electricity bill and peak-to-average ratio (PAR) by 19.94% and 21.55% respectively. Moreover, the HGPO algorithm based home energy management system outperforms the other heuristic algorithms, and further reduces the bill by 25.12% and PAR by 24.88%.
Journal of Energy Storage, 2020
The increase in energy demand, including peak power demand for electricity is one of the most important aspects to be considered in the electricity sector, as it has a negative impact on the flexibility of the power supply and the power balance in the electricity networks. Usually, some distribution system operators impose an additional cost on electricity consumption during peak hours, on other hand, they reducing the electricity price during off-peak periods to encourage householders to reschedule the use of electricity consumption. The work presented in this paper aims to propose an optimal strategy for scheduling energy consumption to help householders for reducing the cost of energy, as well as for saving energy in a residential house connected to a microgrid (Power grid system, PV, and battery storage system). The results presented are obtained using a particle swarm optimization algorithm (PSO) using Matlab. Two optimization scenarios are considered and compared to a base model to prove the efficiency and performance of the proposed optimization model. The results show that the scheduling strategy for energy consumption reduces the daily operating cost by 45% and that about 22% of energy is saved in the system.
Finding optimal schedules in a home energy management system
Electric Power Systems Research, 2020
In this study, we model and solve the scheduling problem embedded in a home energy management system (HEMS), which enables users to overcome the major obstacles in implementing demand response programs. The problem aims to find the minimum energy cost while taking into account the time-varying prices, generation from renewable sources, usage demands for each appliance in household, battery storage capacity and grid constraints. Due to the uncertainties in supply, demand and electricity price, a stochastic optimization approach is utilized. A solution to the problem determines schedules of the operating periods of household appliances, charging cycles of battery storage and plug-in electric vehicles (EVs) and electricity purchase and sale periods for the following days in the decision horizon. We analyze both effects of different price tariffs on HEMS and conduct simulations in order to compare two cases for a green house in terms of energy consumption, where first case is when the house is supported by HEMS and the second one is when the house has no decision support system. Experimental results support the benefit of the usage of the proposed model in a HEMS.
Optimization-based Energy Management System to Minimize Electricity Bill for Residential Customer
2021 3rd International Conference on Electrical & Electronic Engineering (ICEEE), 2021
In order to meet the increasing growth of energy demand, incorporation of renewable resources appears to be a viable solution. With the increase of renewable integration, a big challenge is to handle these intermittent resources in a smart way. An effective approach can be the optimal management of energy of Solar PV panels and energy storage besides the main utility. In this research, an optimization method is employed to manage variable load demand to minimize the overall electricity bill of residential house over a period of 24 hours. Optimization approach schedules the energy resources and decides, when to use grid or cut it off, depending on the availability of solar power, state-of-charge (SOC) of ESS and dynamic electricity tariffs. The impact of using smart energy management system (EMS), is tested by executing three different plausible cases. To verify and validate the real time behaviors of the model, optimization results are compared with the static electricity tariffs and a substantial cost reduction is observed.
Environmental and Climate Technologies
The present study sought to address the scheduling of the grid-connected hybrid energy resources under uncertainty of renewable sources, and load in the residential sector. After introducing hybrid resources, scheduling model was implemented through a power management algorithm in an attempt to optimize resource cost, emissions, and energy not supplied (ENS). The stated problem consists of two decision-making layers with different weight coefficients based on the prioritization of each objective function. The proposed algorithm is selected for energy optimal management based on technical constraints of the dispatchable and non-dispatchable resources, uncertainty parameters and day ahead real time pricing (RTP). Furthermore, the impact of demand response programs (DRP) on the given algorithm was investigated using load shedding and load shifting techniques. Finally, the results obtained led to the optimization of the functions in all decision-making layers with different modes of ope...
Distributed Demand Side Management with Battery Storage for Smart Home Energy Scheduling
Sustainability, 2017
The role of Demand Side Management (DSM) with Distributed Energy Storage (DES) has been gaining attention in recent studies due to the impact of the latter on energy management in the smart grid. In this work, an Energy Scheduling and Distributed Storage (ESDS) algorithm is proposed to be installed into the smart meters of Time-of-Use (TOU) pricing consumers possessing in-home energy storage devices. Source of energy supply to the smart home appliances was optimized between the utility grid and the DES device depending on energy tariff and consumer demand satisfaction information. This is to minimize consumer energy expenditure and maximize demand satisfaction simultaneously. The ESDS algorithm was found to offer consumer-friendly and utility-friendly enhancements to the DSM program such as energy, financial, and investment savings, reduced/eliminated consumer dissatisfaction even at peak periods, Peak-to-Average-Ratio (PAR) demand reduction, grid energy sustainability, socio-economic benefits, and other associated benefits such as environmental-friendliness. Sustainability 2017, 9, 120 2 of 13 work intends, through its proposed DSM algorithm, to offer PDR benefits to the utility with reduced or negligible peak period demand dissatisfaction to consumers, by optimizing energy supply and demand in consumer premises through the incorporation of an in-home DES device. The proposed Energy Scheduling and Distributed Storage (ESDS) algorithm will carry out energy consumption, storage, and expenditure optimization in the smart homes equipped with an in-home DES device. The ESDS algorithm optimizes energy demand and supply in the home between the grid and battery depending on grid energy price and consumer preferences. The ESDS optimization problem was formulated using convex programming and can be installed into smart meters on consumers' premises.
IEEE Access
Energy demand is increasing globally due to the growing human population and progressive lifestyle. The adequate use of available energy resources, including renewable, contributes to a country's economic sustainability and future development. Optimization-based energy management and cost minimization plays a significant role in overcoming energy crises in less developed countries. In this paper, an optimization-based dynamic energy management technique for smart grids is developed based on the integration of available renewable resources and variable consumer demand, distinctive to underdeveloped countries. Consumer demand is classified into fixed, flexible, and highly variable based on population characteristics. In this work, we developed a Dynamic Multiple Knapsack DMKNS algorithm, which automatically schedules energy provision to various users by optimally accounting for the available resources (Grid and Renewable). The proposed method provides a low-cost solution by maintaining a constant energy supply while preserving consumer comfort and grid stability. The simulation results with various intermittent availability of resources using MKNS show a saving up to 50% for a variable energy demand user. The proposed method is general and can also be applied to various underdeveloped regions with similar consumer demand and statistics. INDEX TERMS Optimization-based energy management, dynamic multiple knapsack, energy storage, renewable energy system, smart grid.