Real-time Demand Response Program Research Articles

Ramping of Demand Response Event with Deploying Distinct Programs by an Aggregator

System operators have moved towards the integration of renewable resources. However, these resources make network management unstable as they have variations in produced energy. Thus, some strategic plans, like demand response programs, are required to overcome these concerns. This paper develops an aggregator model with a precise vision of the demand response timeline. The model at first discusses the role of an aggregator, and thereafter is presented an innovative approach to how the aggregator deals with short and real-time demand response programs. A case study is developed for the model using real-time simulator and laboratory resources to survey the performance of the model under practical challenges. The real-time simulation uses an OP5600 machine that controls six laboratory resistive loads. Furthermore, the actual consumption profiles are adapted from the loads with a small-time step to precisely survey the behavior of each load. Also, remuneration costs of the event during the case study have been calculated and compared using both actual and simulated demand reduction profiles in the periods prior to event, such as the ramp period.

Risk-based performance of combined cooling, heating and power (CCHP) integrated with renewable energies using information gap decision theory

The priority of financial aspects of energy systems led the optimization goals to be focused on reducing operation cost as much as possible. The uncertainty of electricity price is one of the main issues of supplying, and modeling the energy systems. To overcome this issue, providing one model, which handles the unpredictable deviations of some basic parameters such as electricity price, is of special importance. In this article, an information gap decision theory (IGDT) has been applied to cope with the electricity price uncertainty. IGDT proposes two functions for two different strategies namely robustness and opportunity which are risk-averse and risk-taker to model the optimization operation in the uncertain environment, respectively. Three energy hubs in a multi-carrier energy system, combined cooling, heating and power (CCHP) integrated with renewable energies such as photovoltaic (PV) and wind turbine (WT) are implemented in a micro energy grid to model this particular system. In addition, the real-time demand response program (DRP) is implemented to manage the loads in different periods. The risk-constrained operation optimization of energy hub model based on proposed IGDT approach has been solved in GAMS software using mixed integer linear programming and results have proven the DRP sufficiency.

Multi‐objective bi‐level optimisation to design real‐time pricing for demand response programs in retail markets

Electric energy consumption is progressively increased requiring new investments in the network to meet. Demand response programs (DRPs) are of practical tools to defer and/or decrease these investments. In this study, a real-time DRP is proposed to promote consumer participation in the energy delivery. The proposed DRP is designed on behalf of the Load Serving Entity (LSE) and aims to maximise its profit. The LSE purchases energy from the up-stream wholesale market and offers to the consumers in the retail market. The consumers, with the objective of maximising the profit, have the choice to purchase energy from the retail market or the LSE. The problem is formulated as a mixed integer linear bi-level programming model wherein LSE and aggregators, on behalf of the consumers, act as the leader and followers of the problem. Also, to avoid creating a new peak profile in light load periods promoted by the designed LSE prices, the upper level problem is tailored as a multi-objective formulation wherein flattening load profile in addition to the maximising LSE profit is considered. The proposed model is implemented on a test case and the results show that the both LSE and consumers benefit is increased with a smoother load profile.

Optimal short-term coordination of water-heat-power nexus incorporating plug-in electric vehicles and real-time demand response programs

In recent years, the population growth has caused the water and energy shortages. Therefore, the current paper proposes a novel method for day-ahead optimization of integrated water-heat-electricity systems with the aim of minimizing the fuel cost of the desalination, heat treatment and power production units. The technical limits of the power only units, combined power and water generation units, cogenerators of heat and electricity, heat only units, and the seawater purification process are incorporated in optimization problem. In addition, a real-time demand side management strategy is applied on heat, pure water and power loads to reduce the objective cost function as low as possible by shifting a percentage of each demand from peak periods to mid or off peak hours. Moreover, aggregators of pure electric vehicles participate in bulk energy management by charging electricity during off-peak low-price hours and discharging it over the on-peak high-price periods. To validate the feasibility and cost-effective performance of the water-heat-power hub system, a mixed-integer non-linear program is developed by generalized algebraic mathematical modelling system (GAMS) optimizer and solved by branch-and-reduce optimization navigator (BARON) tool. The ramp down and ramp up limits of the thermal power plants, generation capacity of water/heat/power producers, and the load-generation balance criterion are considered as optimization constraints. It is found that $659,573 cost saving is obtained due to participation of PEVs and DRPs in trigeneration process.

Optimal performance of CCHP based microgrid considering environmental issue in the presence of real time demand response

In this article, an improved system in micro energy grid is investigated composing of energy hub system as combined cooling, heating and power (CCHP) based microgrid integrated with renewable energies like photovoltaic (PV) and wind turbine (WT). The main goal is to solve a multi-objective model that includes reducing carbon emission and operation cost in the presence of real time demand response program (DRP). The flexibility of suggested energy hub system is the result of utilizing three hubs, storage devices and optimal energy flow. The multi-objective model has been solved using weighted sum approach and max-min fuzzy to make the optimal Pareto solutions and select the trade-off solution, respectively. This paper implemented the priced based real time DRP to flatten the load curve by shifting some amounts of loads from peak periods to off-peak periods. Two cases studied in the article that indicates the effects of real time DRP on optimization of energy hub systems. Finally, the energy hub system model is formulated and solved using CPLEX solver in GAMS optimization software. The results show that the operation cost and carbon emission are reduced 3.97% and 2.26%, respectively due to implementation of real time DRP.

Real-time electricity pricing of a comprehensive demand response model in smart grids

This paper proposes a real-time interactional pricing scheme to maximize the social welfare of players in real-time demand response program in smart grids. Lagrangian relaxation–based dual decomposition is used to separate the social welfare optimization problem into a retailer's problem along with many consumers' subproblems, and the gradient projection method is adopted to solve them. First, the consumers' subproblems are solved to determine the optimal demand responses to the price announced by the retailer. To obtain the optimal demand response, a comprehensive mathematical function is developed on the basis of a combination of 5 costumer's utility functions reported in literature (ie, linear, potential, logarithmic, exponential, and hyperbolic). Afterward, the retailer calculates a real-time price in response to the consumers' reactions to maximize its profit. In terms of practical implementation, the consumers and the retailer interact with each other via a limited number of control messages exchanges to find the optimal solution at each hour. The proposed method is evaluated considering the various retailer's cost functions and the consumers' behaviors. Also, the results of elasticity sensitivity analysis are presented from the retailer and consumer viewpoints.