A Minimal Incentive-based Demand Response Program With Self Reported Baseline Mechanism

In this paper, we propose a novel incentive based Demand Response (DR) program with a self reported baseline mechanism. The System Operator (SO) managing the DR program recruits consumers or aggregators of DR resources. The recruited consumers are required to only report their baseline, which is the minimal information necessary for any DR program. During a DR event, a set of consumers, from this pool of recruited consumers, are randomly selected. The consumers are selected such that the required load reduction is delivered. The selected consumers, who reduce their load, are rewarded for their services and other recruited consumers, who deviate from their reported baseline, are penalized. The randomization in selection and penalty ensure that the baseline inflation is controlled. We also justify that the selection probability can be simultaneously used to control SO's cost. This allows the SO to design the mechanism such that its cost is almost optimal when there are no recruitment costs or at least significantly reduced otherwise. Finally, we also show that the proposed method of self-reported baseline outperforms other baseline estimation methods commonly used in practice

Demand response program implementation methodology: A Colombian study case

The industrialization and urbanization are responsible for Greenhouse Gas (GHG) emissions and could generate energy shortage problems. The application of Demand Response (DR) programs enables the user to be empowered towards a conscious consumption of energy, allowing the reduction or displacement of the demand for electrical energy, contributing to the sustainable development of the sector and the operational efficiency of the electrical system, among others. A reference framework for this type of program is detailed along with a literature survey applied to the Colombian case. The considerations on the design of a methodology to the implementation of the DR pilot, considering if the pilot is in an interconnected system zone or non-interconnected system zone and the application of the design methodology in the modeling of three DR pilots in Colombia is presented. For the modeling of the pilots, the characteristics of the area and the base consumption of the users are considered, and the characteristics and assumptions of the pilot are also defined. Furthermore, the DR pilot in each zone considering four types of users is detailed. The results show the potential for energy reduction and displacement in different time bands for each zone, which allows determining the assessment of the benefits from a technical, financial, and environmental point of view, and the costs of each pilot in monetary terms, it not to compare the pilots with each other, but to illustrate the values that must be taken into account in those analyses. The sensitivity analysis of each pilot was also carried out, considering the variation of the benefit/cost relationship with the energy rate in peak hours vs. off-peak hours and the base energy rate in the area. The sensitivity analysis shows that, when varying the level of energy demand response and the number of pilot participants, the values are presented when the benefit/cost ratio is greater than 1. In addition, the paper provides specific recommendations related to the design of a methodology and the implementation in a pilot DR using simulation

Direct Load Control Demand Response Program for Air Conditioners

According to importance of demand response programs in last decades, many efforts have been made to change the consumption patterns of the users, and the use of renewable resources has also increased. Significant part of energy consumption belongs to the entire kinds of the buildings such as residential, commercial, and office buildings. In this context, the air conditioners can play an important role in demand response programs. Air conditioners can be as thermostatically controllable appliances for direct load control demand response program. In this paper, an optimization algorithm is developed to optimize the power consumption of air conditioners based on the user preferences to maintain the user comfort. The methodology of this work is proposed as a linear optimization problem that consider the generation of a renewable energy resource, which supplies a part of the energy consumption of the building. For the case study, the amount of the renewable energy generation, total consumption of building, and the consumption of the air conditioners in a real research building are considered and the optimization has been done based on the realistic data.This work has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAMGO) and from FEDER Funds through COMPETE program and from National Funds through FCT, under the project UID/EEA/00760/2013.info:eu-repo/semantics/publishedVersio

Harnessing Flexible and Reliable Demand Response Under Customer Uncertainties

Demand response (DR) is a cost-effective and environmentally friendly approach for mitigating the uncertainties in renewable energy integration by taking advantage of the flexibility of customers' demands. However, existing DR programs suffer from either low participation due to strict commitment requirements or not being reliable in voluntary programs. In addition, the capacity planning for energy storage/reserves is traditionally done separately from the demand response program design, which incurs inefficiencies. Moreover, customers often face high uncertainties in their costs in providing demand response, which is not well studied in literature. This paper first models the problem of joint capacity planning and demand response program design by a stochastic optimization problem, which incorporates the uncertainties from renewable energy generation, customer power demands, as well as the customers' costs in providing DR. We propose online DR control policies based on the optimal structures of the offline solution. A distributed algorithm is then developed for implementing the control policies without efficiency loss. We further offer enhanced policy design by allowing flexibilities into the commitment level. We perform real world trace based numerical simulations. Results demonstrate that the proposed algorithms can achieve near optimal social costs, and significant social cost savings compared to baseline methods

Real-Time Demand Response Program Implementation Using Curtailment Service Provider

Nowadays, electricity network operators obligated to utilize the new concepts of power system, such as demand response program, due to peak shaving or reducing the power congestion in the peak periods. These types of management programs have a minimum capacity level for the consumers who tend to participate. This makes small and medium scale consumer incapable to participate in these programs. Therefore, a third party entity, such as a Curtailment Service Provider, can be a solution for this barrier since it is a bridge between the demand side and grid side. This paper provides a real-time simulation of a curtailment service provider that utilize realtime demand response programs for small and medium consumers and prosumers. The case study of the paper represents a network with 220 consumers and 68 distributed generations, which aims at the behavior of two small and medium scale prosumers during a real-time demand response program.This work has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAMGO) and from FEDER Funds through COMPETE program and from National Funds through FCT, under the project UID/EEA/00760/2013.info:eu-repo/semantics/publishedVersio

Load Shifting Implementation in a Laundry Room under Demand Response Program

In order to overcome the consequences of high energy consumption, renewable energy resources advancement, smart grids development, and demand response programs effectiveness should be noticed and surveyed. Regarding the important role of buildings in energy consumption, another important factor is equipping the buildings and houses with automation. For this purpose, this paper presents an optimization algorithm based on demand response program which is concerning about time of use for ancillary service and also is based on energy price in different periods. In the case study of this paper, the power consumption of two washing machines and one dryer are considered to achieve the demand response program goal. Since it is not reasonable to use dryer before washing machines, the sequence of operation cycle of devices is important for the algorithm. The obtained results of the algorithm show the applied load shifting based on energy price, demand response requirements and remunerationThis work has received funding from Portugal 2020 under SPEAR project (NORTE-01-0247-FEDER-040224) and from FEDER Funds through COMPETE program and from National Funds through (FCT) under the project UIDB/00760/2020, and CEECIND/02887/2017.info:eu-repo/semantics/publishedVersio

Demand Response Program Integrated With Electrical Energy Storage Systems for Residential Consumers

This article presents a distributed resilient demand response program integrated with electrical energy storage systems for residential consumers to maximize their comfort level. A dynamic real-time pricing method is proposed to determine the hourly electricity prices and schedule the electricity consumption of smart home appliances and energy storage systems commitment. The algorithm is employed in normal and emergency operating conditions, taking into account the comfort level of consumers. In emergency conditions, the power outage of consumers is modeled for different hours and outage patterns. To evaluate the applicability of the proposed model, real samples of Southern California households are considered to model the smart homes and their appliances. Further, a sensitivity analysis is performed to assess the impacts of the number of households and number of persons per household on the output results. The results showed that the proposed model reduced the costs of utility in normal and emergency conditions by about 33.77% and 30.92%, respectively. The values of total payments of consumers in normal and emergency conditions were decreased by about 34.26% and 31.31%, respectively. Further, the consumers comfort level for normal and emergency conditions increased by about 146.78% and 110.2%, respectively. Finally, the social welfare for normal and emergency conditions increased by about 46% and 49.06%, respectively.© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Distributed, Agent-Based Intelligent System for Demand Response Program Simulation in Smart Grids

A distributed, agent-based intelligent system models and simulates a smart grid using physical players and computationally simulated agents. The proposed system can assess the impact of demand response programs

Application of distinct demand response program during the ramping and sustained response period

The environmental concerns around energy, namely electricity, have driven attention to innovative approaches to fostering consumers participation in the whole energy system management. Accordingly, the concept of demand response provides incentives and signals no consumers to change the normal consumption patterns to increase the use of renewables, for example. The problem is that such response of consumers has a large amount of uncertainty. This paper proposes a methodology in which different demand response programs are activated and deactivated during an event to cover the demand response deviations from the target. Even after achieving the response target, if the actual response of consumers is reduced to a critical level, additional programs are activated. The proposed approach considers consumers participating in an aggregate way, supported by an aggregator. The case study in this paper accommodates three demand response programs, showing how different consumers are activated and remunerated for the provision of consumption reduction. It has been seen that the proposed methodology is flexible as desired to accommodate the uncertainty of consumers’ responses.This work has received funding from FEDER Funds through COMPETE program and from National Funds through (FCT) under the project COLORS (PTDC/EEI-EEE/28967/2017). The work has been done also in the scope of projects UIDB/00760/2020, and CEECIND/02887/2017, financed by FEDER Funds through COMPETE program and from National Funds through (FCT) . The authors would like to acknowledge the contribution of Omid Abrishambaf to this workinfo:eu-repo/semantics/publishedVersio

Evaluation of Power System Reliability Considering Direct Load Control Effects

With the development of deregulated power systems and increase of prices in some hours of day, demand side management programs were noticed more by customers. In restructured power systems, DSM programs are introduced as DEMAND RESPONSE. In this paper we try to evaluate the effect of DR programs on power system reliability and nodal reliability. In order to reach to this target, Direct Load Control program, as the most common demand response program, is considered. Effects of demand response programs on system and nodal reliability of a deregulated power system are investigated using direct load control and economic load model, DC power-flow-based optimal load curtailment objective and reliability evaluation techniques. The proposed method is evaluated by numerical studies based on a small reliability test system (RBTS), and simulation results show that demand response program can improve the system and nodal reliability.DOI:http://dx.doi.org/10.11591/ijece.v3i2.229