Electricity Scheduling for Residential Prosumers with Demand Response (original) (raw)
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Energies, 2020
Smart grid (SG) is a next-generation grid which is responsible for changing the lifestyle of modern society. It avoids the shortcomings of traditional grids by incorporating new technologies in the existing grids. In this paper, we have presented SG in detail with its features, advantages, and architecture. The demand side management techniques used in smart grid are also presented. With the wide usage of domestic appliances in homes, the residential users need to optimize the appliance scheduling strategies. These strategies require the consumer's flexibility and awareness. Optimization of the power demand for home appliances is a challenge faced by both utility and consumers, particularly during peak hours when the consumption of electricity is on the higher side. Therefore, utility companies have introduced various time-varying incentives and dynamic pricing schemes that provides different rates of electricity at different times depending on consumption. The residential appliance scheduling problem (RASP) is the problem of scheduling appliances at appropriate periods considering the pricing schemes. The objectives of RASP are to minimize electricity cost (EC) of users, minimize the peak-to-average ratio (PAR), and improve the user satisfaction (US) level by minimizing waiting times for the appliances. Various methods have been studied for energy management in residential sectors which encourage the users to schedule their appliances efficiently. This paper aims to give an overview of optimization techniques for residential appliance scheduling. The reviewed studies are classified into classical techniques, heuristic approaches, and meta-heuristic algorithms. Based on this overview, the future research directions are proposed.
IEEE Access
Demand response (DR) shaves peak energy consumption and drives energy conservation to ensure reliable operation of power grid. With the emergence of the smart power grid (SPG), DR has become increasingly popular and highly contributes to energy optimization. On this note, in this work, DR is adopted for scheduling home appliances to reduce utility bill payment, peak to average demand ratio (PADR), and discomfort. First, home appliances are classified into two categories according to time and power flexibility: time-flexible and power-flexible. Secondly, the demand-side users power usage scheduling problem is modelled as per the user priority and modes of operation considering demand and supply. Finally, the energy consumption scheduler (ECS) is developed to adjust the time and power of both types of appliances under different operation modes to acquire desired tradeoff between utility bills payment and discomfort, and PADR and discomfort. Simulation results depict that employing the proposed ECS benefits demand-side users by minimizing their utility bills payment, PADR, and achieving the desired tradeoff between utility bill payment and discomfort, and PADR and discomfort. Results illustrate that developed model reduced utility bill payment and alleviated PADR without compromising comfort by 28% and 21%, respectively, compared to without scheduling case.
Optimised Residential Loads Scheduling Based on Dynamic Pricing of Electricity : A Simulation Study
2013
This paper presents a simulation study which addresses Demand Side Management (DSM) via scheduling and optimization of a set of residential smart appliances under day-ahead variable pricing with the aim of minimizing the customer’s energy bill. The appliances’ operation and the overall model are subject to the manufacturer and user specific constraints formulated as a constrained linear programming problem. The overall model is simulated using MATLAB and SIMULINK / SimPowerSystems basic blocks. The results comparing Real Time Pricing (RTP) and the Fixed Time Tariff (FTT) demonstrate that optimal scheduling of the residential smart appliances can potentially result in energy cost savings. The extension of the model to incorporate renewable energy resources and storage system is also discussed. Keywords-Demand Side Management; Optimization; Linear Programming; Real Time Pricing; Smart Appliances
Optimized Residential Loads Scheduling Based On Dynamic Pricing Of Electricity : A Simulation Study
This paper presents a simulation study which addresses Demand Side Management (DSM) via scheduling and optimization of a set of residential smart appliances under day-ahead variable pricing with the aim of minimizing the customer's energy bill. The appliances' operation and the overall model are subject to the manufacturer and user specific constraints formulated as a constrained linear programming problem. The overall model is simulated using MATLAB and SIMULINK / SimPowerSystems basic blocks. The results comparing Real Time Pricing (RTP) and the Fixed Time Tariff (FTT) demonstrate that optimal scheduling of the residential smart appliances can potentially result in energy cost savings. The extension of the model to incorporate renewable energy resources and storage system is also discussed.
Energies
The residential sector is a major contributor to the global energy demand. The energy demand for the residential sector is expected to increase substantially in the next few decades. As the residential sector is responsible for almost 40% of overall electricity consumption, the demand response solution is considered the most effective and reliable solution to meet the growing energy demands. Home energy management systems (HEMSs) help manage the electricity demand to optimize energy consumption without compromising consumer comfort. HEMSs operate according to multiple criteria, including electricity cost, peak load reduction, consumer comfort, social welfare, environmental factors, etc. The residential appliance scheduling problem (RASP) is defined as the problem of scheduling household appliances in an efficient manner at appropriate periods with respect to dynamic pricing schemes and incentives provided by utilities. The objectives of RASP are to minimize electricity cost and peak...
Energies
With recent developments, smart grids assured for residential customers the opportunity to schedule smart home appliances’ operation times to simultaneously reduce both the electricity bill and the PAR based on demand response, as well as increasing user comfort. It is clear that the multi-objective combinatorial optimization problem involves constraints and the consumer’s preferences, and the solution to the problem is a difficult task. There have been a limited number of investigations carried out so far to solve the indicated problems using metaheuristic techniques like particle swarm optimization, mixed-integer linear programming, and the grey wolf and crow search optimization algorithms, etc. Due to the on/off control of smart home appliances, binary-coded genetic algorithms seem to be a well-fitted approach to obtain an optimal solution. It can be said that the novelty of this work is to represent the on/off state of the smart home appliance with a binary string which undergoe...
Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing
In this paper, we present an energy optimization technique to schedule three types of household appliances (user dependent, interactive schedulable and unschedulable) in response to the dynamic behaviours of customers, electricity prices and weather conditions. Our optimization technique schedules household appliances in real time to optimally control their energy consumption, such that the electricity bills of end users are reduced while not compromising on user comfort. More specifically, we use the binary multiple knapsack problem formulation technique to design an objective function, which is solved via the constraint optimization technique. Simulation results show that average aggregated energy savings with and without considering the human presence control system are 11.77% and 5.91%, respectively.
Proceedings of 2012 IEEE 17th International Conference on Emerging Technologies & Factory Automation (ETFA 2012), 2012
This paper focuses on the development of load scheduling algorithms applied to representative types of home appliances, in order to reduce the energy cost, in a context of fluctuating energy prices, based on user's requirements. The presented algorithms could be executed both in a centralized energy management system and in distributed low-cost load controllers. Respective analysis and evaluation provide significant insight, both concerning feasibility and flexibility of such approaches as well as potential cost reduction.
Optimal scheduling of household appliances for demand response
2014
In this paper, residential demand response is studied through the scheduling of typical home appliances in order to minimize electricity cost and earn the relevant incentive. A mixed integer nonlinear optimization model is built under a time-of-use electricity tariff. A case study shows that a household is able to shift consumption in response to the varying prices and incentives, through which the consumer may realize an electricity cost saving of more than 25%. It has also been shown that at different values of the weighting factor α gives varying costs, from which the consumer is able to choose according to their preferences. Therefore a final decision about participation in the program could be made.
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.