Price Discounts and Consumer Load-Shifting Behavior in the Smart Grid

This paper studies the impact of consumers' individual attitudes towards load shifting in electricity consumption in an electricity market that includes a single electricity provider and multiple consumers. A Stackelberg game model is formulated in which the provider uses price discounts over a finite number of periods in order to induce incentives for consumers to shift their peak period loads to off-peak periods. The equilibrium outcomes are investigated and the analytical results are derived for this type of market, where not only the response behaviors of independent consumers are diverse but also an individual consumer's valuation of electricity consumption varies across periods. The obtained results demonstrate that consumer sensitivities to price discounts significantly impact price discounts and load-shifts, which are not necessarily monotonic. The authors also observe that a diverse market leads to lower peak-to-average values and provider payoffs compared to a homogenous market unless the latter one is composed of consumers with relatively lower inconvenience costs during the peak periods.

The role of demand response in residential electricity load reduction using appliance shifting techniques

Purpose Some of the major concerns since the implementation of smart meters (prepaid meters) in some parts of Ghana is how electricity consumers have benefited from data obtained from these meters by providing important statistics on electricity-saving advice; this is one of the key demand-side management methods for achieving load reduction in residential homes. Appliance shifting techniques have proved to be an effective demand response strategy in load reduction. The purpose of this paper is therefore to help consumers of electricity understand when and how they can shift some appliances from peak to off-peak and vice versa. Design/methodology/approach The research uses an analysis technique of Richardson et al. (2010). In their survey on time-of-use surveys to determine the usage of electricity in households as far as appliance shifting was concerned, this study allowed for the assessment of how the occupants’ daily activities in households affect residential electricity consumption. Fell et al. (2014) modeled an aggregate of electricity demand using different appliances (n) in the household. The data for the peak time used in this study were identified from 05:00 to 08:00 and 17:00 to 21:00 for testing the load shifting algorithms, and the off-peak times were pecked from 10:00 to 16:00 and 23:00. This study technique used load management considering real-time scheduling for peak levels in the selected homes. The household devices are modeled in terms of controlled parameters. Using this study’s time-triggered loads on refrigerators and air conditioning systems, the findings suggested that peak loads can be reduced to 45% as a means of maintaining the simultaneous quality of service. To minimize peak loads to around 35% or more, Chaiwongsa and Wongwises (2020) have indicated that room air conditioning and refrigerator loads are simpler to move compared to other household appliances such as cooking appliances. Yet in conclusion, this study made a strong case that a decrease in household peak demand for electricity is primarily contingent on improvements in human behavior. Findings This study has shown that appliance load shifting is a very good way of reducing electrical consumption in residential homes. The comparative performance shows a moderate reduction of 1% in load as was found in the work done by Laicaine (2014). The results, however, indicate that load shifting to a large extent can be achieved by consumer behavioral change. The main response to this study is to advise policymakers in Ghana to develop the appropriate demand response and consumer education towards the general reduction in electrical load in domestic households. The difficulty, however, is how to get the attention of consumer’s on how to start using appliances with less load at peak and also shift some appliances from off-peak times. By increasing consumer knowledge and participation in demand response, it is possible to achieve more efficiency and flexibility in load reduction. The findings were benchmarked with existing comparison studies but may benefit from the potential production of structured references. However, the findings show that load shifting can only be done by modifying consumer actions. Research limitations/implications It should be remembered that this study showed that the use of appliances shifting in residential homes results in load reduction benefits for customers, expressed as savings in electricity prices. The next step will be to build on this cost/benefit study to explain and measure how these reductions transform into net consumer gains for all Ghanaian households. Practical/implications Load shifting will include load controllers in the future, which would automatically handle electricity consumption from various appliances in the home. Based on the device and user needs, the controllers can prioritize loads and appliance usage. The algorithms that underpin automatic load controllers will include knowledge about the behaviors of groups of end users. The results on the time dependency of activities may theoretically inform the algorithms of automatic demand controllers. Originality/value This paper addresses an important need for the country in the midst of finding solutions to an unending energy crisis. This paper presents demand response to the Ghanaian electricity consumer as a means to help in the reduction of load in residential homes. This is a novel research as no one has at yet carried out any research in this direction in Ghana. This paper has some new information to offer in the field of demand in household electricity consumption.

Demand Side Management for Commercial Area Using Teaching Learning Based Optimization

In Smart Grid Demand side management (DSM) plays a crucial role which permits customers to form educated selections concerning their energy consumption. It allows the strength to companies lessen the height load call for and reshape the burden profile. Most of the present demand aspect management ways utilized in ancient energy management system is with specific techniques and algorithms. In addition, the present ways handle solely a restricted range of governable a lot of restricted varieties of loads. This paper covers a requirement aspect management strategy supported load shifting technique for demand aspect management of future sensible grids with an outsized range of devices of many sorts. The day-in advance load shifting technique is proposed and mathematically formulated as a minimization problem. Teaching Learning Based Optimization (TLBO) is an efficient optimization is proposed. Considering Smart Grid with commercial customer, Simulations has been carried out. The respective results emphasis that the considered demand side management strategy attains substantial savings, whereas suppresses the mark of load demand of the smart grid. The outcome is by improve in sustainability of the smart grid, in addition to reduced standard operational value and carbon emission levels. The proposed algorithms can be easily applied to various optimization problems.

Nontargeted vs. Targeted vs. Smart Load Shifting Using Heat Pump Water Heaters

Deployment of CTA-2045–enabled devices is increasing in the U.S. market. These devices allow utilities or third-party aggregators to control appliance energy use in homes, and could also be applied to end uses in small commercial buildings. This study focuses on a field study using CTA-2045–enabled water heaters to shift electric load off the peak and toward periods when renewable resources are more prevalent (e.g., near noon for solar resources and near midnight for wind resources). The following load shifting strategies were compared to understand effects on the aggregate load-shifting capabilities of Heat Pump Water Heaters (HPWHs) and on consumer hot water supply: non-targeted (traditional), targeted (grouped, with different shifting schedules) and “smart” (adaptive control commands). The results of this study show that targeted and smart control strategies yield significantly more load-shifting potential from a population of water heaters than the non-targeted approach without sacrificing hot water supply to occupants. However, as control commands become more aggressive, aggregators may face challenges in meeting consumer hot water demand. The findings and lessons learned can benefit electric utilities and inform updates to manufacturer controls and communications standards. The data collected may also be useful for developing and validating HPWH models.

Research on Peak Load Shifting Operation Strategy of Small-Scale Mobile Energy Storage Device in Distribution Network

This paper studied two methods of using energy storage (ES) to achieve peak load shifting by charging and discharging. In different distribution network transformer power supply areas, the time of peak and valley of load is not identical. The small-scale mobile ES device was commanded to deliver energy at the peak of the power supply area to reduce the supply pressure of the power system by central control units (CCU), and while the load is at the valley, the ES charges and absorbs power to enhance the utilization rate of energy in the system. Currently, the control methods of peak load shifting for ES device (Constant Power Control and Threshold Control) were studied, and the operation results in power system were analyzed with their involvement, which provided the substantial supports for CCU to dispatch. Ultimately, the results showed the methods proposed were feasible and effective by simulation.

Information batteries

Coping with the intermittency of renewable power is a fundamental challenge, with load shifting and grid-scale storage as key responses. We propose Information Batteries (IB), in which energy is stored in the form of information---specifically, the results of completed computational tasks. Information Batteries thus provide storage through speculative load shifting, anticipating computation that will be performed in the future. We take a distributed systems perspective, and evaluate the extent to which an IB storage system can be made practical through augmentation of compiler toolchains, key-value stores, and other important elements in modern hyper-scale compute. In particular, we implement one specific IB prototype by augmenting the Rust compiler to enable transparent function-level precomputation and caching. We evaluate the overheads this imposes, along with macro-level job prediction and power prediction. We also evaluate the space of operation for an IB system, to identify the best case efficiency of any IB system for a given power and compute regime.