The smart grid concept continues to evolve and various methods have been developed to enhance the energy efficiency of the electricity infrastructure. Demand Response (DR) is considered as the most cost-effective and reliable solution for the smoothing of the demand curve, when the system is under stress. DR refers to a procedure that is applied to motivate changes in the customers' power consumption habits, in response to incentives regarding the electricity prices. In this paper, we provide a comprehensive review of various DR schemes and programs, based on the motivations offered to the consumers to participate in the program. We classify the proposed DR schemes according to their control mechanism, to the motivations offered to reduce the power consumption and to the DR decision variable. We also present various optimization models for the optimal control of the DR strategies that have been proposed so far. These models are also categorized, based on the target of the optimization procedure. The key aspects that should be considered in the optimization problem are the system's constraints and the computational complexity of the applied optimization algorithm.

A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms

It is expected that electric vehicles (EVs) will soon represent a large share of the demand for electricity. Several research works have extolled the advantages of these devices as flexible demands, not only to charge their batteries when it is cheaper to do so, but also to provide services in the form of vehicle-to-grid (V2G) power injections to the system. These services, however, could reduce the useful life of the battery and thus introduce a cost that needs to be taken into account when scheduling the charging of these vehicles. This study presents a scheduling algorithm for EVs under a real time pricing scheme with uncertainty. The objective function explicitly takes into account the cost of battery degradation not only when used to provide services to the system but also in terms of the EV utilisation for motion. The results show that the scheduling of the V2G services is sensitive to the electricity prices uncertainty and to the degradation costs derived from the energy arbitrage. Also, the optimal energy state of charge of the batteries is highly dependent on whether the cost of battery degradation is taken into account or not.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-gtd.2013.0624

Optimal scheduling of electric vehicle charging and vehicle-to-grid services at household level including battery degradation and price uncertainty

Unlocking the €53 billion savings from smart meters in the EU: How increasing the adoption of dynamic tariffs could make or break the EU’s smart grid investment

It is anticipated that an uncoordinated operation of individual home energy management (HEM) systems in a neighborhood would have a rebound effect on the aggregate demand profile. To address this issue, this paper proposes a coordinated home energy management (CoHEM) architecture in which distributed HEM units collaborate with each other in order to keep the demand and supply balanced in their neighborhood. Assuming the energy requests by customers are random in time, we formulate the proposed CoHEM design as a multi-stage stochastic optimization problem. We propose novel models to describe the deferrable appliance load [e.g., plug-in (hybrid) electric vehicles (PHEV)], and apply approximation and decomposition techniques to handle the considered design problem in a decentralized fashion. The developed decentralized CoHEM algorithm allow the customers to locally compute their scheduling solutions using domestic user information and with message exchange between their neighbors only. Extensive simulation results demonstrate that the proposed CoHEM architecture can effectively improve real-time power balancing. Extensions to joint power procurement and real-time CoHEM scheduling are also presented.

Real-Time Power Balancing Via Decentralized Coordinated Home Energy Scheduling

The problem of dynamic pricing of electricity in a retail market is considered. A Stackelberg game is used to model interactions between a retailer and its customers; the retailer sets the day-ahead hourly price of electricity and consumers adjust real-time consumptions to maximize individual consumer surplus. For thermostatic demands, the optimal aggregated demand is shown to be an affine function of the day-ahead hourly price. A complete characterization of the tradeoffs between consumer surplus and retail profit is obtained. The Pareto front of achievable tradeoffs is shown to be concave, and each point on the Pareto front is achieved by an optimal day-ahead hourly price. Effects of integrating renewables and local storage are analyzed. It is shown that benefits of renewable integration all go to the retailer when the capacity of renewable is relatively small. As the capacity increases beyond a certain threshold, the benefit from renewable that goes to consumers increases.

Dynamic Pricing and Distributed Energy Management for Demand Response

The Power of Dynamic Pricing

As the Smart Grid takes shape, it opens new vistas for change. One of those salient opportunities for change that is enabled by the Smart Grid is the pricing of electricity. By and large, existing rate designs hide the temporal variation in the cost of electricity and thereby promote over-consumption of electricity during peak times and under-consumption during off-peak times. In much of North America, the problem is especially pronounced during the top 60-100 hours of the year which may account for as much as 10-18 percent of system peak load. In order to meet this critical peak load, expensive combustion turbines are purchased and installed, raising rates for all customers. Dynamic pricing rate designs can remedy this problem and enhance economic efficiency. For that reason, they are receiving increased attention by state commissions throughout the country. California has made a major commitment to it, by approving the deploying of advanced metering infrastructure (AMI) and by establishing critical-peak pricing (CPP) rates as the default tariff for all non-residential customer classes with AMI. Other smart rate designs, such as real-time pricing, may be provided as options. To show the power of dynamic pricing, we develop a set of illustrative rates using data from a generic California utility and compute the benefits that would accrue to the state's economy from widespread deployment of these rates. While the numbers are specific to California, the process and methodology are perfectly general and should be of interest to utilities and regulatory bodies throughout North America. We develop dynamic pricing rates for four customer classes: Residential, Medium Commercial and Industrial (C&I), Large Commercial, and Large Industrial. In order to show the development of these rates, we begin with a discussion of existing rates. All the dynamic pricing rates are developed to be revenue neutral to these existing rates.

John Tsoukalis

Papers

The Power of Dynamic Pricing

The Power of Dynamic Pricing