Task-Oriented Communication for Real-Time Demand Response in Smart Grids

Coordinated optimization scheduling operation of integrated energy system considering demand response and carbon trading mechanism

In the face of the considerable volatility in electricity market prices, retail electric providers (REPs) occupy the difficult position between the utility and the end-user. They purchase electricity either through long-term contracts, in the day-ahead or spot markets, or self-generate. This electricity is then sold to the end-user at a fixed price. Thus, REPs bear the financial burden of electricity price uncertainty. In a market where prices can increase by orders of magnitude in as little as 15 min, single spikes have driven some REPs to bankruptcy. To mitigate the negative effects of market volatility, REPs can employ demand response (DR), shifting consumers’ electricity load from periods of high prices to lower-priced time periods. While DR comes in many forms, we focus on thermostatically-controlled residential DR, made possible through internet-connected thermostats. The timing of residential heating and cooling load makes it a particularly financially advantageous target for DR. We present a dynamic programming (DP) formulation designed to optimally schedule DR events to maximize their savings for the REP. We also consider the effects of price uncertainty on the savings through the use of stochastic dynamic programming (SDP). Our case study examines the profitability of thermostatically-controlled DR, specifically of air-conditioning (A/C) load, in the Electricity Reliability Council of Texas (ERCOT). We find that optimal management of thermostatically-controlled DR can generate significant savings to the REP, potentially improving their profit margins as much as 25%. However, we also find that few events generate most savings, necessitating precise scheduling facilitated by our formulation. Electricity price uncertainty marginally reduces these expected savings, but optimally-scheduled DR still offers REPs an effective method of mitigating risk.

Real-time demand response (DR) in smart μgrid has been shown to be an effective tool for frequency regulation with increased penetration of renewable energy resources into the grid. Since DR is recognized as an incentive or direct payment to the participants, it is consequently desired to minimize the cost of DR for the utility. This paper presents an optimal DR strategy for minimizing the cost of DR for the utility in smart grid era. The economic model developed by Pennsylvania/New Jersey/Maryland (PJM) utility in the USA is used on an IEEE 13-bus standard system. Simulation results verify the effectiveness of the proposed approach to minimize the cost of DR for the utility. It is also shown that the DR, with or without optimization, decreases the overall cost of frequency regulation for the utility compared to the conventional spinning reserve, without sacrificing system stability.

Demand side management will be a key component of future smart grid that can help reduce peak load and adapt elastic demand to fluctuating generations. In this paper, we consider customers that operate different appliances and propose a demand response approach based on utility maximization. Each appliance provides a certain benefit depending on the pattern or volume of power it consumes. Each customer wishes to optimally schedule its power consumption so as to maximize its individual net benefit subject to various consumption and power flow constraints. We show that there exist time-varying prices that can align individual optimality with social optimality, i.e., under such prices, when the customers selfishly optimize their own benefits, they automatically also maximize the social welfare. The utility company can thus use dynamic pricing to coordinate demand responses to the benefit of the overall system. We propose a distributed algorithm for the utility company and the customers to jointly compute this optimal prices and demand schedules. Finally, we present simulation results that illustrate several interesting properties of the proposed scheme.

An effective price mechanism is needed in the Smart Grid to induce demand response. This paper analyses the deficiencies of the traditional price-based demand response mechanism in terms of 1) the information elements in which price is bound with time, and 2) the risk-sharing structure in which consumers bear little price risks. A Piecewise Price based on Equivalent Load (PPEL) mechanism is proposed, in which the fluctuation of wind power is converted to equivalent load, and the electricity pricing is based on tracing the equivalent load. The PPEL is not only reasonable in risk-sharing and risk-controlling, but also effective in reflecting the supply-demand relation and the true value of electricity. This provides strong incentive for wind power integration. The case study based on the 24-hour load profile of a typical summer-day from one province of China and wind power output data from NREL of the U.S. demonstrates the effectiveness of the proposed mechanism.

The Special Issue “Demand Response in Smart Grids” includes 11 papers on a variety of topics. The success of this Special Issue demonstrates the relevance of demand response programs and events in the operation of power and energy systems at both the distribution level and at the wide power system level. This reprint addresses the design, implementation, and operation of demand response programs, with focus on methods and techniques to achieve an optimized operation as well as on the electricity consumer.

In current scenario in electric power systems, customer participation of Demand Response in grid operations and management has been gaining importance in Smart Grid with the advent of information communication technology. In this paper an Automatic Generation Control (AGC) scheme that includes price based demand response scheme is proposed. The price based demand response scheme finds the optimal thermostat set point of the Thermostatically Controlled Load (TCL) for a change in price issued by Independent system operator for AGC. This paper provides a load model that includes demand response as a transfer function that provides the change in power consumed due to the price based demand response of aggregated TCLs using coupled Fokker-Planck equations. Thus the main work of this paper is the load modelling of power gain from the demand response of aggregated TCL units for AGC. Simulation results show that AGC in single area system with demand response improves system performance and leads to economic operation of the system as generators operate close to their fixed power loading.

Electric Vehicles (EVs), as well as the number of applications for their management, are rapidly increasing due to the fact that Internet of Vehicles (IoV) becomes more informative and industrialized. According to the literature, IoV requires decentralization towards the energy Demand Response (DR) management, with secure energy trading, efficient charging scheduling, and incentives for making the drivers to participate. Blockchain and Distributed Ledger Technologies (DLTs) can be used to enable a foundational environment to support DR management. However, and to the best of our knowledge, none of the existing research works discussed in the literature adopts a holistic approach to address them. As such there is a demand for exploring a unified blockchain-based framework for distributed DR management. Therefore, the aim of this work is focused on the blockchain adoption in IoV-assisted smart cities. More specifically, we propose a blockchain based approach, to be used for building a secure and user-centric DR management framework. Our proposition aims to address the DRP in IoV through charging scheduling based on the generation of EV driving profiles, and optimal Vehicle-to-Vehicle/Grid (V2V | V2G) energy trading.

The increased uncertainty of the electric grid due to the penetration of renewable energy sources and deregulation of the electric market is aimed to be alleviated by demand response (DR) in the future smart grid. The demand-side resources can be incentivized to alter their consumption patterns by varying their electricity price over time. A major residential energy demand contribution is from electric heating, which, when combined with smart energy storage using water heaters, could be utilized to defer consumption to more inexpensive periods without affecting the customer's thermal quality of service. The objective is to optimize the consumer electricity price of electric storage space heating customers, in order to maximize the profit of the retailer. This approach of varying the customer electricity prices leads to a game-theoretic scenario, where the procurement and consumption profiles of the retailer and consumer agents are based on the set electricity price. The optimization of the consumer electricity price is shown to offer lesser expense for the retailer. In addition, hourly load-following can be improved by offering further discounts for the consumers.

This paper proposes an economic dispatch strategy for the electricity system with one generation company, multiple utility companies and multiple consumers, which participate in demand response to keep the electricity real-time balance. In the wholesale markets, multiple utility companies will commonly select a reliable agent to negotiate with the generation company on the wholesale price. It is challengeable to find a wholesale price to run the electricity market fairly and effectively. In this study, we use the multiple utility companies’ profits to denote the utility function of the agent and formulate the interaction between the agent and the generation company as a bargaining problem, where the wholesale price was enforced in the bargaining outcome. Then, the Raiffa–Kalai–Smorodinsky bargaining solution (RBS) was utilized to achieve the fair and optimal outcome. In the retail markets, the unfavorable disturbances exist in the power management and price when the consumers participate in the demand response to keep the electricity real-time balance, which motivates us to further consider the dynamic power management algorithm with the additive disturbances, and then obtain the optimal power consumption and optimal retail price. Based on the consumers’ utility maximization, we establish a price regulation model with price feedback in the electricity retail markets, and then use the iterative algorithm to solve the optimal retail price and the consumer’s optimal power consumption. Hence, the input-to-state stability condition with additive electricity measurement disturbance and price disturbance is given. Numerical results demonstrate the effectiveness of the economic dispatch strategy.

Investigating the Impacts of Modeling Variables- A Case Study with Smart Grid Demand Response

Photovoltaic sizing assessment for microgrid communities under load shifting constraints and endogenous electricity prices: A Stackelberg approach

ABSTRACTThe reaction capacity of the demand side would influence the price volatility in the electricity supply and demand scenario in the spot market because of the differences in user involvement in the market. To enhance the power market's stability and dependability, a demand‐side resource aggregation response optimization model is designed considering the load adjustment ability of users. First of all, a demand‐side response architecture covering various forms of user participation is established, and the demand response mechanism of users in different forms of participation under this architecture is analyzed. To guarantee the sustainability of the demand response mechanism in this architecture, the market clearing model taking into account the active response of indirect consumers is created, and the appropriate settlement method is suggested. Then, the revenue problem of demand‐side resource aggregators in the power market is mapped to the bidding decision problem, and the bidding optimization model of demand response under two contract modes of load reduction and load transfer is proposed. The findings indicate that demand response can improve the reliability of the distribution network and user benefit under the selected index and verify the rationality and effectiveness of the proposed method.

Demand response program with real-time pricing can encourage electricity users toward scheduling their energy usage to off-peak hours. A user needs to schedule the energy usage of his appliances in an online manner since he may not know the energy prices and the demand of his appliances ahead of time. In this paper, we study the users’ long-term load scheduling problem and model the changes of the price information and load demand as a Markov decision process, which enables us to capture the interactions among users as a partially observable stochastic game. To make the problem tractable, we approximate the users’ optimal scheduling policy by the Markov perfect equilibrium (MPE) of a fully observable stochastic game with incomplete information. We develop an online load scheduling learning (LSL) algorithm based on the actor-critic method to determine the users’ MPE policy. When compared with the benchmark of not performing demand response, simulation results show that the LSL algorithm can reduce the expected cost of users and the peak-to-average ratio in the aggregate load by 28% and 13%, respectively. When compared with the short-term scheduling policies, the users with the long-term policies can reduce their expected cost by 17%.

In the smart grid scenario, Demand Response (DR) and Demand Side Management (DSM) programs are considered as strategic to obtain a more efficient operation of the grid. The flexibility given by the final customers plays a key role to unlock the potential benefits offered by the application of these schemes. A classical example of flexible load that can be exploited for DR and DSM purposes is the electric heat pump. This paper aims at evaluating the main factors affecting the flexibility available in the management of electric heat pumps for space heating. The performed analysis allows identifying some indexes to quantify the available flexibility and highlights how the thermal comfort requirements of the customers affect the provided level of flexibility. Sample simulations show the impact of these flexibility terms on the results of a DSM program designed for power peak shaving at grid level. The possible use of the defined indexes for sorting the customers flexibility and for estimating the potential benefits offered by the adopted DSM scheme is also investigated and discussed.