Optimization algorithms for home energy resource scheduling in presence of data uncertainty (original) (raw)
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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.
2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems, 2010
We describe a decision-support tool that optimizes the energy services of residential end-users by scheduling the operation of available distributed energy resources. We discuss the application of the tool to a 'smart' home case study and the solution to the resulting highly-dimensional scheduling problem. We then use the optimal schedules formulated by the tool to determine the value of the forecasted information used when the schedules are created. This is achieved by computing the additional costs avoided by the end-users due to the accuracy of the forecasts. We also demonstrate how to use the tool to derive robust schedules when the end-users are not certain on the magnitude of solar insolation, magnitude of energy service demands, availability of a plug-in hybrid vehicle as storage, and status of Critical Peak Pricing. The robust schedule is derived by maximizing the expected net benefit when the schedule is applied to all likely scenario outcomes.
Smart residential energy scheduling utilizing two stage Mixed Integer Linear Programming
2015 North American Power Symposium (NAPS), 2015
In this paper, we design and evaluate the feasibility of a system which minimizes residential electricity cost of individual homes by shifting demand over a daily forecast price cycle. Ideally, our system will accept use-time preferences from consumers and optimize their appliances' operation around those given patterns. However, using the system to recommend optimum use-times to consumers is also possible by accepting ideal use preferences from an external load manager and computing the cost savings of these preferences relative to the cost of the consumer's current use patterns.
Automated Energy Scheduling Algorithms for Residential Demand Response Systems
Demand response technology is a key technology for distributing electricity tasks in response to electricity prices in a smart grid system. In the current demand response research, there has been much demand for an automated energy scheduling scheme that uses smart devices for residential customers in the smart grid. In this paper, two automated energy scheduling schemes are proposed for residential smart grid demand response systems: semi-automated scheduling and fully-automated scheduling. If it is possible to set the appliance preference, semi-automated scheduling will be conducted, and if it is impossible, fully-automated scheduling will be operated. The formulated optimization problems consider the electricity bill along with the user convenience. For the fully-automated scheduling, the appliance preference can automatically be found according to appliance type from the electricity consumption statistics. A performance evaluation validates that the proposed scheme shifts operation to avoid peak load, that the electricity bill is significantly reduced, and that user convenience is satisfied.
This paper describes a methodology for making robust day-ahead operational schedules for controllable residential distributed energy resources (DER) using a novel energy service decision support tool. The tool is based on the consumers deriving benefit from energy services and not on electric energy. It maximizes consumer net benefit by scheduling the operation of DER. The robust schedule is derived using a stochastic programming approach formulated for the DER scheduler: the objective function describing the consumer net benefit is maximized over a set of scenarios that model the range of uncertainty. The optimal scenario set is derived using heuristic scenario reduction techniques. Robust operational schedules are formulated for a 'smart' home case study with four controllable DER under stochastic energy service demand, availability of storage DER, and status of dynamic peak pricing. The robust schedule results in a lower expected cost but at the expense of long computation times. The computation period however is not much of a disadvantage because schedules are computed off-line. The consumer can prepare several DER schedules and simply choose the one to implement according to their perception of the coming day. The robust schedules are formulated using an improved version of co-evolutionary particle swarm optimization.
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...
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.
Artificial intelligent-based optimization of automated home energy management systems
With the tendency to move toward automated home energy management systems, the cooperative capacity of the smart grid "SG" became more and more significant. Distributed generation "DG", in addition to the novel patterns of electricity production, enabled the two-way energy flow. The concern of this paper is on introducing an automated control technique for optimizing the operational performance of the DG units within the residential applications. This problem is formulated in a constrained-nonlinear optimization structure based on a detailed economic system. Genetic algorithm "GA" technique is used to solve this problem by defining the optimal settings of the DG units. Availability of two-way communication and the use of smart meters will enable online implementation and facilitate data collection within a fully automated system. Based on certain tariffs and realistic load curves, many scenarios of operation are analyzed and assessed. The results show that using optimization technique in cooperative with advanced smart meters can enhance the economic situation of the overall grid significantly.
Electricity Scheduling for Residential Prosumers with Demand Response
2019
The advent of smart grid provided ample opportunities for consumers to adopt small-scale renewable energy generation and become prosumers. In addition to this, advancement in information, communication and control technologies has equipped prosumers with smart home appliances. To extract energy saving and lesser cost of electricity, residential prosumers perform energy management in accordance with renewable energy generation, energy storage, responsive appliances, and electricity price. This requires optimal scheduling of prosumer demand with their operational preferences of appliances in order to perform energy saving. In this regard, this paper proposes a novel optimization based control of different (characteristics) appliances to schedule electricity for residential prosumers. Prosumer demand preferences for appliances are considered with operational constraints of appliances. Time-of-use tariff and dayahead real time pricing is used for electricity scheduling and its impact is...
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.