This paper, by comparing three potential energy trading systems, studies the feasibility of integrating a community energy storage (CES) device with consumer-owned photovoltaic (PV) systems for demand-side management of a residential neighborhood area network. We consider a fully-competitive CES operator in a non-cooperative Stackelberg game, a benevolent CES operator that has socially favorable regulations with competitive users, and a centralized cooperative CES operator that minimizes the total community energy cost. The former two game-theoretic systems consider that the CES operator first maximizes their revenue by setting a price signal and trading energy with the grid. Then the users with PV panels play a non-cooperative repeated game following the actions of the CES operator to trade energy with the CES device and the grid to minimize energy costs. The centralized CES operator cooperates with the users to minimize the total community energy cost without appropriate incentives. The non-cooperative Stackelberg game with the fully-competitive CES operator has a unique Stackelberg equilibrium at which the CES operator maximizes revenue and users obtain unique Pareto-optimal Nash equilibrium CES energy trading strategies. Extensive simulations show that the fully-competitive CES model gives the best trade-off of operating environment between the CES operator and the users.
In this paper, we study Demand Response (DR) problematics for different levels of information sharing in a smart grid. We propose a dynamic pricing scheme incentivizing consumers to achieve an aggregate load profile suitable for utilities, and study how close they can get to an ideal flat profile depending on how much information they share. When customers can share all their load profiles, we provide a distributed algorithm, set up as a cooperative game between consumers, which significantly reduces the total cost and peak-to-average ratio (PAR) of the system. In the absence of full information sharing (for reasons of privacy), when users have only access to the instantaneous total load on the grid, we provide distributed stochastic strategies that successfully exploit this information to improve the overall load profile. Simulation results confirm that these solutions efficiently benefit from information sharing within the grid and reduce both the total cost and PAR. Outdoor temperature, thermal comfort level of consumers and payback load effect constrain direct load control (DLC) of air-conditioning loads and limit the DLC schedule. Since the constraints in direct air-conditioning load control are characteristics of air-conditioning loads, this work presents a novel group-DLC program with a least enthalpy estimation (LEE)-based thermal comfort controller for air-conditioning systems to control air-conditioning systems and eliminate DLC problems simultaneously. The g-DLC controller is the threshold for problems between air-conditioning units and the load management program, and arranges the DLC schedule for all air-conditioning units. The LEE-based thermal comfort controller can maintain the thermal comfort level within a reasonable range and prolong off-shift time of the DLC program, thereby increasing shedding load. It can also mitigate the impact of outdoor temperature and prevent payback load effect. Hence, DLC constraints on air-conditioning loads are mitigated. Considerable developments in the real-time telemetry of demand-side systems allow independent system operators (ISOs) to use reserves provided by demand response (DR) in ancillary service markets. Currently, many ISOs have designed programs to utilize the reserve provided by DR in electricity markets. This paper presents a stochastic model to schedule reserves provided by DR in the wholesale electricity markets. Demand-side reserve is supplied by demand response providers (DRPs), which have the responsibility of aggregating and managing customer responses. A mixed-integer representation of reserve provided by DRPs and its associated cost function are used in the proposed stochastic model. The proposed stochastic model is formulated as a two-stage stochastic mixed-integer programming (SMIP) problem. The first-stage involves network-constrained unit commitment in the base case and the second-stage investigates security assurance in system scenarios. The proposed model would schedule reserves provided by DRPs and determine commitment states of generating units and their scheduled energy and spinning reserves in the scheduling horizon. The proposed approach is applied to two test systems to illustrate the benefits of implementing demand-side reserve in electricity markets. Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, recent studies have revealed that the lack of knowledge among users about how to respond to time-varying prices as well as the lack of effective building automation systems are two major barriers for fully utilizing the potential benefits of real-time pricing tariffs. We tackle these problems by proposing an optimal and automatic residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the electricity payment and minimizing the waiting time for the operation of each appliance in household in presence of a real-time pricing tariff combined with inclining block rates. Our design requires minimum effort from the users and is based on simple linear programming computations. Moreover, we argue that any residential load control strategy in real-time electricity pricing environments requires price prediction capabilities. This is particularly true if the utility companies provide price information only one or two hours ahead of time. By applying a simple and efficient weighted average price prediction filter to the actual hourly-based price values used by the Illinois Power Company from January 2007 to December 2009, we obtain the optimal choices of the coefficients for each day of the week to be used by the price predictor filter. Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load scenarios. Therefore, the deployment of the proposed optimal energy consumption scheduling schemes is beneficial for both end users and utility companies. Electrolytic process, employed for manufacturing basic chemicals like caustic soda and chlorine, is highly energy intensive. Due to escalating costs of fossil fuels and capacity addition, the electricity cost has been increasing for the last few decades. Electricity intensive industries find it very difficult to cope up with higher electricity charges particularly with time-of-use (TOU) tariffs implemented by the utilities with the objective of flattening the load curve. Load management programs focusing on reduced electricity use at the time of utility's peak demand, by strategic load shifting, is a viable option for industries to reduce their electricity cost. This paper presents an optimization model and formulation for load management for electrolytic process industries. The formulation utilizes mixed integer nonlinear programming (MINLP) technique for minimizing the electricity cost and reducing the peak demand, by rescheduling the loads, satisfying the industry constraints. The case study of a typical caustic-chlorine plant shows that a reduction of about 19 in the peak demand with a corresponding saving of about 3.9 in the electricity cost is possible with the optimal load scheduling under TOU tariff. Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges.