The depleting fossil fuel and environmental concerns have created a revolutionary movement towards the installation and utilization of Renewable Energy Sources (RES) such as wind and solar energy. The RES entails challenges, both in regards to the physical integration into a grid system and regarding management of the expected demand. The flexibility in energy demand can facilitate the alignment of the supply and demand to achieve a dynamic Demand Response (DR). The flexibility is often not explicitly available or provided by a user and has to be analyzed and extracted automatically from historical consumption data. The predictive analytics of consumption data can reveal interesting patterns and periodicities that facilitate the effective extraction and representation of flexibility. The device-level analysis captures the atomic flexibilities in energy demand and provides the largest possible solution space to generate demand/supply schedules. The presence of stochasticity and noise in the device-level consumption data and the unavailability of contextual information makes the analytics task challenging. Hence, it is essential to design predictive analytical techniques that work at an atomic data granularity and perform various analyses on the effectiveness of the proposed techniques. The Ph.D. study is sponsored by the TotalFlex Project (http://www.totalflex.dk/) and is part of the IT4BI-DC program with Aalborg University and TU Dresden as Home and Host University, respectively. The main objective of the TotalFlex project is to develop a cost-effective, market-based system that utilizes total flexibility in energy demand, and provide financial and environmental benefits to all involved parties. The flexibilities from various devices are modeled using a unified format called a flex-offer, which facilitates, e.g., aggregation and trading in the energy market. In this regards, this Ph.D. study focuses on the predictive analytics of the historical device operation behavior of consumers for an efficient and effective extraction of flexibilities in their energy demands. First, the thesis performs a comprehensive survey of state-of-the-art work in the literature. It presents a critical review and analysis of various previously proposed approaches, algorithms, and methods in the field of user behavior analysis, forecasting, and flexibility analysis. Then, the thesis details the flexibility and flex-offer concepts and formally discusses the terminologies used throughout the thesis. Second, the thesis contributes to a comprehensive analysis of energy consumption behavior at the device-level. The key motive of the analysis is to extract device operation patterns of users, the correlation between devices operations, and influence of external factors in device-level demands. A novel cost/benefit trade-off analysis of device flexibility is performed to categorize devices into various segments according to their flexibility potential. Moreover, device-specific data preprocessing steps are proposed to clean device-level raw data into a format suitable for flexibility analysis. Third, the thesis presents various prediction models that are specifically tuned for device-level energy demand prediction. Further, it contributes to the feature engineering aspect of generating additional features from a demand consumption timeseries that effectively capture device operation preferences and patterns. The demand predictions utilize the carefully crafted features and other contextual information to improve the performance of the prediction models. Further, various demand prediction models are evaluated to determine the model, forecast horizon, and data granularity best suited for the device-level flexibility analysis. Furthermore, the effect of the forecast accuracy on flexibility-based DR is evaluated to identify an error level a market can absorb maintaining profitability. Fourth, the thesis proposes a generalized process for automated generation and evaluation of flex-offers from the three types of household devices, namely Wet-devices, Electric Vehicles (EV), and Heat Pumps. The proposed process automatically predicts and estimates times and values of device-specific events representing flexibility in its operations. The predicted events are combined to generate flex-offers for the device future operations. Moreover, the actual flexibility potential of household devices is quantified for various contextual conditions and degree days. Fifth, the thesis presents user-comfort oriented prescriptive techniques to prescribe flex-offers schedules. The proposed scheduler considers the trade-off between both social and financial aspects during scheduling of flex-offers, i.e., maximizing the financial benefits in a market and at the same time minimizing the loss of user comfort. Moreover, it also provides a distance-aware error measure that quantifies the actual performance of forecast models designed for flex-offers generation and scheduling. Sixth, the thesis contributes to the comprehensive analysis of the financial viability of device-level flexibility for dynamic balancing of demand and supply. The thesis quantifies the financial benefits of flexibility and investigates the device type specific market that maximizes the potential of flexibility, both regarding DR and financial incentives. Henceforth, a financial analysis of each proposed technique, namely forecast model, flex-offer generation model, and flex-offer scheduling is performed. The key motive is to evaluate the usability of the proposed models in the device-level flexibility based DR scheme and their potential in generating a positive financial incentive to markets and customers. Seven, the thesis presents a benchmark platform for device-level demand prediction. The platform provides the research community with a centralized repository of device-level datasets, forecast models, and functionalities that facilitate comparisons, evaluations, and validation of device-level forecast models. The results of the thesis can contribute to the energy market in materializing the vision of utilizing consumption and production flexibility to obtain dynamic energy balance. The developed demand forecast and flex-offer generation models also contribute to the energy data analytics and data mining fields. The quantification of flexibility further contributes by demonstrating the feasibility and financial benefits of flexibility-based DR. The developed experimental platform provide researchers and practitioners with the resources required for device-level demand analytics and prediction.

Fuel supply issues are a major concern in remote island communities and this is an engineering field that needs to be analyzed in detail for transition to sustainable energy systems. Power generation in remote communities such as the islands of the Maldives relies on power generation systems primarily dependent on diesel generators. As a consequence, power generation is easily disrupted by factors such as the delay in transportation of diesel or rises in fuel price, which limits shipment quantity. People living in remote communities experience power outages often, but find them just as disruptive as people who are connected to national power grids. The use of renewable energy sources could help to improve this situation, however, such systems require huge initial investments. Remote power systems often operate with the help of financial support from profit-making private agencies and government funding. Therefore, investing in such hybrid systems is uncommon. Current electrical power generation systems operating in remote communities adopt an open loop control system, where the power supplier generates power according to customer demand. In the event of generation constraints, the supplier has no choice but to limit the power supplied and this often results in power cuts. Most smart grids that are being established in developed grids adopt a closed loop feedback control system. The smart grids integrated with demand side management tools enable the power supplier to keep customers informed about their daily energy consumption. Electric utility companies use different demand response techniques to achieve peak energy demand reduction by eliciting behavior change. Their feedback information is commonly based on factors such as cost of energy, environmental concerns (carbon dioxide intensity) and the risk of black-outs due to peak loads. However, there is no information available on the significant link between the constraints in resources and the feedback to the customers. In resource-constrained power grids such as those in remote areas, there is a critical relationship between customer demand and the availability of power generation resources. This thesis develops a feedback control strategy that can be adopted by the electrical power suppliers to manage a resource-constrained remote electric power grid such that the most essential load requirements of the customers are always met. The control design introduces a new concept of demand response called participatory demand response (PDR). PDR technique involves cooperative behavior of the entire community to achieve quality of life objectives. It proposes the idea that if customers understand the level of constraint faced by the supplier, they will voluntarily participate in managing their loads, rather than just responding to a rise in the cost of energy. Implementation of the PDR design in a mini-grid consists of four main steps. First, the end-use loads have to be characterized using energy audits, and then they have to be classified further into three different levels of essentiality. Second, the utility records have to be obtained and the hourly variation factors for the appliances have to be calculated. Third, the reference demand curves have to be generated. Finally, the operator control system has to be designed and applied to train the utility operators. A PDR case study was conducted in the Maldives, on the island of Fenfushi. The results show that a significant reduction in energy use was achieved by implementing the PDR design on the island. The overall results from five different constraint scenarios practiced on the island showed that during medium constrained situations, load reductions varied between 4.5kW (5.8%) and 7.7kW (11.3%). A reduction of as much as 10.7kW (15%) was achieved from the community during a severely constrained situation.

Master of Science / Department of Electrical and Computer Engineering / Anil Pahwa / With the Smart Grid trend setting in, various techniques to make the existing grid smarter are being considered. The price of electricity is one of the major factors, which affects the electric utility as well as the numerous consumers connected to the grid. Therefore deciding the right price of electricity for the time of day would be an important decision to make. Consumers’ response to this change in price will impact peak demand as well as their own annual energy bill. Owning a small wind generator under the Critical Peak Pricing (CPP) and Time of Use (TOU) price-based demand response programs could be a viable option. Economic evaluation of owning a small wind generator under the two pricing schemes, namely Critical Peak Pricing (CPP) and Time of Use (TOU), is the main focus of this research. Analysis shows that adopting either of the pricing schemes will not change the annual energy bill for the consumer. Taking into account the installed cost of the turbine, it may not be significantly economical for a residential homeowner to own a small wind turbine with either of the pricing schemes in effect under the conditions assumed.

Renewable and green energy development is vigorously supported by most countries to suppress the continuously increasing greenhouse gas (GHG) emissions. However, as the total renewable capacity expands, the growth rate of emissions is not effectively restrained. An unforeseen factor contributing to this growth is the regulation service, which aims to mitigate power frequency deviations caused by the intermittent renewable power generation and unbalanced power supply and demand. Regulation services, normally issued by supply-side balancing authorities, leads to inefficient operations of regulating generators, thus directly contributing to the emissions growth. Therefore, it is urged to find solutions that can stabilize the power frequency with an increased energy using efficiency. Demand response (DR) is an ideal candidate to solve this problem. The current smart grid infrastructure enables a high penetration of smart residential electric loads, including heating, ventilation, and air conditioning systems (HVACs), air conditioners (A/Cs), electric water heaters (EWHs), and plug-in hybrid electric vehicles (PHEVs). Beyond simply drawing power from the grid for local electric demand, those loads can also adjust their power consumption patterns by responding to the control signals sent to them. It has been proved that, if appropriately aggregated and controlled, power consumption of demand-side residential loads possesses a huge potential for providing regulation services. The research of DR is pivotal from the the application perspective due to the efficient usage of renewable energy generation and the high power quality. However, many problems remain open in this area due to the load heterogeneity, device physical constraints, and computational and communication restrictions. In order to move one step further toward industry applications, this PhD thesis is concerned with two cruxes in DR program design: Aggregation Modeling and Control; it deals with two main types of terminal loads: Thermostatically Controlled Appliances (TCAs) (Chapters 2-4) and PHEVs (Chapter 5). This thesis proceeds with Chapter 1 by reviewing the state-of-the-art of DR. Then in Chapter 2, the focus is put on modeling and control of TCAs for secondary frequency control. In order to explicitly describe local TCA dynamics and to provide the aggregator a clear global view, TCAs are aggregated by directly stacking their individual dynamics. Terminal TCAs are assumed in a general case that an arbitrary number of TCAs are equipped with varying frequency drives (VFDs). A centralized model predictive control (MPC) scheme is firstly constructed. In the design, to tackle the TCA lockout effect and to facilitate the MPC scheme, a novel approach for converting time-integrated interdependent logic constraints into inequality constraints are proposed. Since a centralized MPC scheme may introduce non-trivial computational load by using this aggregation model, especially when the number of TCAs increases, a distributed MPC (DMPC) scheme is proposed. This DMPC scheme is validated through a more practical case study that all TCAs are subject to pure ON/OFF control. Chapter 3 targets on aggregation modeling and control of TCAs for the provision of primary frequency control. To efficiently reduce the computational load to facilitate the primary frequency control, the explicit monitoring of terminal TCAs must be compromised. To this end, a 2-D population-based model is proposed, in which TCAs are clustered into state bins according to their temperature information and running status. Within the proposed aggregation framework, individual TCA dynamics' evolutions develop into TCA population migration probabilities, thus the computational load of the centralized controller is dramatically reduced. Based on this model, a centralized MPC scheme is proposed for the primary frequency control. The previously proposed population-based model provides a promising direction for the centralized control. However, in traditional population-based model, TCA lockout effect can only be considered when implementing the control signals. This will cause a mismatch between the nominal control signals and the actually implemented ones. To conquer this, in Chapter 4, an improved population-based model is studied to explicitly formulate the TCA lockout effect in the aggregation model. A DMPC scheme is firstly constructed based on this model. Furthermore, since the predictions of regulation signals may not be available or they may include severe disturbances, a control scheme that does not require future regulation signals is urged. To this end, an optimal control scheme, in which a novel penalty is included to maximize the regulation capability, is proposed to facilitate the most practical scenario. Another type of terminal loads that has a huge potential in providing grid services is PHEV. At this point, Chapter 5 presents the aggregation and charging control of heterogeneous PHEVs for the provision of DR. In contrast to using battery state-of-charge (SOC) solely as the system state, a new aggregation model is proposed by introducing a novel concept, i.e., charging requirement index. This index combines the SOC with drivers' specified charging requirements, thus inherently providing the aggregation model with richer information. A centralized MPC scheme is proposed based on this novel model. Both of the model and controller are validated through an overnight valley-filling case study. Finally, the conclusions of the thesis are summarized and future research topics are presented. / Graduate / 0537 / 0544 / 0548 / mingxiliu419@gmail.com

The objective of this research is to present a comprehensive framework for harnessing the flexibility of power systems in the presence of unforeseen events, such as those associated with component outages or renewable energy variability. Increased penetration of variable resources in the power grid, mainly in the form of wind and solar plants, has resulted in variable power flow patterns, increased thermal unit cycling and higher reserve capacity requirements. Furthermore, the variability of renewable energy output has increased the system’s ramping requirements and threatens the system’s voltage control capabilities. However, new sources of flexibility and network control are emerging to address these problems. Specifically, energy storage systems, demand side management, distributed energy resources and flexible transmission operation can participate by providing ramping services and/or voltage control, as well as by alleviating transmission congestion. This research focuses on contributing to modeling and optimization approaches for scheduling the operation of these sources of flexibility in a certain look-ahead horizon, ensuring a state of the art level of modeling accuracy, with full inclusion of voltage control considerations which do not exist in current DC-OPF modeling approaches. Also, by including reactive power flows, the network congestion model proposed is above par compared to the current state-of-the-art for look-ahead dispatch literature. Nevertheless, the model is further expanded by including a thermal model for transmission lines, which allows for the implementation of dynamic line ratings in look-ahead economic dispatch. The benefits from these augmented modeling capabilities are documented and compared with current operating practices. Once an AC-OPF look-ahead optimization problem has been established, and the corresponding components have been modeled, further contributions are made in the area of remedial action schemes. The developed formulations allow for the identification of appropriate corrective actions that will restore feasibility in infeasible cases. Finally, a combination of contingency filtering and contingency analysis approaches is developed, to allow for fast identification and analysis of critical outages in the transmission system. The filtering approach is based on a basic Taylor expansion of network power flow equations as well as a new formulation of margin indices that directly quantify the proximity to constraint violation in the post-outage system state. The analysis approach is based on low-rank modifications of the Jacobian matrix of network equations, to produce good estimates of post-outage operating states and map the effect on the system’s operating constraints. Compared to current state of the art, advances are made both in the speed and the accuracy of the analysis, since the proposed filtering and analysis methods are fully unbalanced. The need for unbalanced security analysis is discussed and justified. Through the contributions made in this research, a roadmap to increase flexibility in power system operations is developed. Namely, an enhanced modeling capability allows for integration of additional sources of flexibility and voltage control and a highly accurate security analysis and remedial actions formulation allows for improved response to unforeseen critical outages and rapid generation changes.

Recently, more and more disputations about how demand response should be compensated have arisen. Moreover, the court is about to rehear the Order 745. It probably will have significant impact on the whole working system used to be built for demand response before. Nowadays, some power companies and utilities think that they will endure profits leakage while demand response resources still are compensated. In this research, knowledge of demand response, local marginal price, Order 745 and other related concept will be explained in detail in case of misunderstanding. Associated with all these knowledge, a possible compensation method will be proposed. It combines many existing compensation methods. It mainly can be divided into three parts, i.e., high load period, off-peak period and low load period. The demand response resources will be compensated appropriately through these three periods. The compensation method endeavors to be just and reasonable.

Peak demand is an issue in power supply system when demand exceeds the available capacity. Continuous growth in peak demand increases the risk of power failures, and increases the marginal cost of supply. The contribution of the residential sector to the system peak is quite substantial and has been a subject of discussion internationally. For example, a study done in New Zealand in 2007 attributed about half of system peak load to the residential sector. International research has attributed a significant influence of human behaviour on households energy use. “Demand Response” is a demand side management tool aimed at achieving peak energy demand reduction by eliciting behaviour change. It encompasses energy needs analysis, information provision to customers, behaviour induction, smart metering, and new signalling and feedback concepts. Demand response is far advanced in the industrial and commercial demand sectors. In the residential sector, information barriers and a lack of proper understanding of consumers’ behaviour have impeded the development of effective response strategies and new enabling technologies in the sector. To date, efforts to understanding residential sector behaviour for the purpose of peak demand analysis has been based on pricing mechanism. However, not much is known about the significance of other factors in influencing household customers’ peak electricity demand behaviour. There is a tremendous amount of data that can be analyzed and fed back to the user to influence behaviour. These may include information about energy shortages, supply security and environmental concerns during the peak hours. This research is intended to begin the process of understanding the importance of some of these factors in the arena of peak energy consumption behaviour. Using stated preference survey and focus group discussions, information about household customers’ energy use activities during winter morning and evening peak hours was collected. Data about how customers would modify their usage behaviour when they receive enhanced supply constraint information was also collected. The thesis further explores households’ customer demand response motivation with respect to three factors: cost (price), environment (CO2-intensity) and security (risk of black-outs). Householders were first informed about the relationship between these factors and peak demand. Their responses were analyzed as multi-mode motivation to energy use behaviour change. Overall, the findings suggest that, household customers would be willing to reduce their peak electricity demand when they are given clear and enhanced information. In terms of motivation to reduce demand the results show customers response to the security factor to be on par with the price factor. The Environmental factor also produced a strong response; nearly two-thirds of that of price or security. A generic modelling methodology was developed to estimate the impact of households’ activity demand response on the load curve of the utility using a combination of published literature reviews and resources, and own research work. This modelling methodology was applied in a case study in Halswell, a small neighbourhood in Christchurch, New Zealand, with approximately 400 households. The results show that a program to develop the necessary technology and provide credible information and understandable signals about risks and consequences of peak demand could provide up to about 13% voluntary demand reduction during the morning peak hours and 8% during the evening peak hours.

Rapid advances in the smart grid technology are making it possible to tackle a lot of problems in the aged power systems. High-speed data acquisition system, high-voltage power electronic equipment, advanced utility and customer interaction technologies, as well as distributed renewable generation are enabling the revolution in the electric power generation, delivery and distribution. Through the implementation of ubiquitous metering and communication networks, the customers would no longer be a passive receiver of the electrical energy, but instead, an active participant in the power system and electricity market. They can not only sell their own energy to the utility, but also take part in the emergency restoration in the power grid. Nonetheless, some technical barriers are encountered during this revolution, such as difficulties in integrating home automation, smart metering, customer interaction and power system operation into the whole system. This thesis proposes a customer involved load shedding algorithm for both the power system frequency control and the micro-grid islanding. This new algorithm possesses the features of centralized load control and distributed load control, which fully utilizes the advantages of hierarchical communication networks along with the home automation. The proposed algorithm considers the reliability of the power grid as well as the comfort of the electricity users. In the power distribution system, the high-level control centre is responsible for coordinating the local load controllers, whilst the local controller takes charge of frequency monitoring and decision making. In the micro-grid, a centralized control strategy is adopted to better serve the system with the wide set of information available at the micro-grid control centre. The simulation results have demonstrated the correctness and feasibility of the proposed algorithm. Finally, the hardware implementation further tests the validity of the wireless sensor networks serving as the system’s monitoring and communication technology. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2013-09-24 20:01:37.098

Os consumidores residenciais de energia elétrica no Brasil pagam um preço constante pela mesma em qualquer horário do dia, a despeito da variação constante nos custos de oferta. Isto não é economicamente eficiente. Para se atingir esta eficiência a implantação de uma tarifa inteligente se faz necessária, questão mais factível com o advento das redes inteligentes. Este trabalho busca antever se este desenvolvimento é custo efetivo ou não. Em primeiro lugar, os conceitos de redes inteligentes e de medidores avançados são apresentados. Em segundo lugar, são apresentados os conceitos de resposta da demanda e se demonstra porque o preço da eletricidade, para o consumidor final, deve ser maior na ponta do que fora da ponta. Por fim, se busca fazer uma análise custo benefício de um projeto hipotético de Infraestrutura de Leitura Avançada, desenvolvido por uma distribuidora de energia da região Centro Oeste do Brasil, a partir do estudo de cenários. Esse projeto hipotético ocorre num horizonte de dez anos, entre 2014 e 2023. O primeiro passo foi o desenvolvimento de campanhas de medição entre os anos de 2012 e 2013. Usando os dados aí obtidos, duas curvas de carga horárias foram desenvolvidas, uma para os dias úteis e a outra para finais de semana e feriados. O horário de pico é entre as 19 e as 22 horas nos dias úteis, e das 18 as 23 horas nos finais de semana e feriados. O custo da oferta e o consumo total de eletricidade foram obtidos, respectivamente, no Operador Nacional do Sistema e na Agência Nacional de Energia Elétrica. Os resultados obtidos em 15 experimentos prévios foram usados para estimar as hipotéticas elasticidades preço e elasticidades de substituição. Duas modalidades tarifárias foram testadas nos cenários: Tarifa Pelo Horário de Uso e Tarifa Pelo Horário de Uso com Preço de Pico Crítico. Os resultados obtidos ficaram aquém dos conceitualmente previstos. Uma análise é feita para tentar entender a razão desta resposta. / Residential customers in Brazil pay a constant price throughout the day, despite the large time variation in costs of supply. It is not economically efficient. It is necessary to set it to costumers with smart rates, and this possibility is getting closer from the development of smart grids. This work aims understand in advance if this deployment is cost-effective or not. Firstly, the concepts of Smart Grids, AMR (Automatic Meter Reading) and AMI (Advanced Metering Infrastructure) are presented. Secondly, concepts of demand response are described, and there is a demonstration of the reasons why electricity peak prices must be higher than off-peak prices. Thirdly, we seek to make a cost-benefit analysis for a hypothetical AMI project installation to residential customers, served by a utility in the Middle West of Brazil, under some potential scenarios. This hypothetical project runs in a ten year horizon (2014-2023). The first step was to perform measurement campaigns in 2012 and 2013. Using the data obtained, two residential hourly load curves were developed, one for weekdays and another for weekends and holidays. Peak time occurs between 7 and 10 PM in weekdays, and from 6 to 11 PM on weekends and holidays. The cost of supply and total consumption in the residential segment were obtained, respectively, from the Brazilian National System Operator (ONS) and Electric Energy Agency (ANEEL). The results obtained in fifteen previous experiments were used to estimate hypotheticals price elasticity and elasticity of substitution. Two types of rates were tested in scenarios: TOU and TOU with CPP. The results were lower than expected. An analysis is made to try to understand the reasons for this answer.

Em diversos mercados de energia onde há competição, a formação de preços de energia elétrica no mercado de curto prazo decorre do equilíbrio da oferta e da demanda, onde geradores e grandes consumidores informam, em periodicidade horária ou inferior, as quantidades de energia e preços associados aos quais estão dispostos a produzir e consumir, respectivamente. No Brasil, no entanto, a demanda utilizada no modelo de formação de preço de energia elétrica no curto prazo (PLD) é considerada inelástica em relação ao preço. Por mais que se possam constatar sinais de resposta da demanda frente à volatilidade do PLD, ou ao custo com uso da rede de transmissão e distribuição no período de ponta, não há mecanismo estabelecido para que os consumidores ofertem as quantidades de energia e preços aos quais estão dispostos a reduzir seu consumo. O presente trabalho tem o objetivo de apresentar proposta de alteração no processo de formação de preço no curto prazo de modo a permitir a Oferta da Redução do Consumo (ORC) pelos consumidores industriais. A proposta parte da representação do parque termelétrico atual, que serve de base para o valor da oferta de redução do consumo, as adaptações para introdução da curva de operação para fins de consideração da redução de consumo, bem como metodologia para aferição do montante de energia efetivamente reduzido. Além disso, de modo a apresentar o potencial benefício sistêmico com a introdução da proposta, são apresentadas simulações com a cadeia de modelos de formação de preço atual tendo como base a indústria de alumínio no Brasil. Os cenários de ORC da indústria levam em consideração parâmetros econômicos que asseguram a atratividade do negócio em consonância com o benefício sistêmico de redução do custo de operação. Apresenta-se ainda simulação da operação do ano de 2015 com estimativa do potencial de ORC no Ambiente de Contratação Livre (ACL) onde se constatou reduções de até 25% no Custo Marginal de Operação (CMO) e 16% de redução despacho termelétrico. / In several competitive power markets, short term power price is the result of the balance of supply and demand represented by bid and ask prices and energy quantities. In Brazil, short term power price (PLD) calculated by Newave/Decomp price models consider price-inflexible demand, even though traces of demand response to short term power prices and demand tariffs can be identified. The purpose of this Thesis is the proposal of changes in process of power pricing allowing large energy consumers bid their price to curtail their consumption in substitution of thermal power dispatch. Topics included in proposal are: cost of installed thermal power plants in power system, industrial demand curtail and restart features, and demand response effectiveness appraisal tools. Current power price models were employed on simulations to evaluate system\'s benefits with demand response. From an industrial perspective, accounting measures were basis to convert loss of production in demand-side bidding price in order to keep business profitability. Estimate of demand side bidding potential market in Brazilian free market with simulation of system impact in 2015 with results that reached 25% of Marginal Cost reduction and 16% of Thermal Dispatch reduction.