A Stochastic Decision-Making Model for an Electricity Retailer With Intermittent Renewable Energy and Short-Term Demand Response

Prado, Josue Campos do; Wang, Qiao

doi:10.1109/tsg.2018.2805326

Cited by 74 publications

(35 citation statements)

References 29 publications

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“…within area i are set in Equation 3, where 5, respectively. Then, the first constraints in Equations (6) and 7represent in the intra-day a load should be kept on/off for at least T I i,off /T I i,on continuous time before it can be turned off/on, where T I i,on and T I i,off are maximum continuous interruption and operation time. The second constraints in Equations (6) and 7represent in the inter-day a load should meet the maximum continuous interruption and operation time requirement.…”

Section: Monthly Robust Stochastic Optimizationmentioning

confidence: 99%

“…In [5], a monthly energy deviation trade mode with the participation of interruptible loads was established. In [6], a bi-level model of electricity purchase and sale strategies for brokers was proposed by taking user's demand response technology into account. The brokers utilized a short-term demand response to avoid power unbalances and reduce the power purchase risk.…”

Section: Introductionmentioning

confidence: 99%

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A Power Exchange Strategy for Multiple Areas with Hydro Power and Flexible Loads

et al. 2019

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Areas with hydro power may purchase extra power from the outside power market during dry seasons, which will cause a deviation between the actual and expected power purchase amount due to the inaccurate judgment of the market situation. Because of the uncertainty of price fluctuations, the risk of purchasing power in the real-time market to eliminate this deviation is very high. This paper proposes an innovative trade mode, where the power exchange strategy between multiple areas is adopted through forming an alliance, i.e., one area can use the controllable elements within others, and constructing a monthly and post day-ahead two phase optimization model. The objective function of the monthly stochastic robust optimization considers the power purchase cost to determine the controllable elements dispatch dates for every area in the alliance. Thus, areas can make reasonable dispatch schedules for controllable elements to avoid the resource waste that means more controllable elements are prepared before post day-ahead optimization but less are used after post day-ahead optimization. While the post day-ahead optimization model determines the internal controllable elements dispatch and power exchange amount after the day-ahead market clearing process, users’ satisfaction and dispatch schedule changes for energy storage device are also considered. In order to solve the proposed two phase model, the dual principle and linearization methods are used to convert them into mixed-integer linear programming problems that can be effectively solved by the Cplex solver. The study case verifies the power deviation cost decreases with the power exchange strategy and the important role of energy storage devices.

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Section: Monthly Robust Stochastic Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Power Exchange Strategy for Multiple Areas with Hydro Power and Flexible Loads

et al. 2019

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“…For example, without compromising on their levels of functionality, the charging processes of electric vehicles and residential pool pumps [4,5], can be modulated, suspended, and/or resumed appropriately to aid in the supply/demand balance. Substantial relevant results have been recorded in the literature [6][7][8][9][10][11]. Particularly, the GRIP (grids with intelligent periphery) is proposed in [12], which conceptually presents a platform so as to implement a variety of applications regarding demand response.…”

Section: Introductionmentioning

confidence: 99%

Market implementation of multiple-arrival multiple-deadline differentiated energy services

Chen

Varaiya

2020

Automatica

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An increasing concern in power systems is on how to elicit flexibilities in demands for better supply/demand balance. To this end, several differentiated energy services have been put forward, wherein demands are discriminated by their different flexibility levels. Motivated by the duration-differentiated energy services, we have proposed energy services differentiated by durations, arrival times, and deadlines. The purpose of this paper is to study the market implementation of such multiplearrival multiple-deadline differentiated energy services. To verify the economic feasibility, we establish that in a forward market, there exists an efficient competitive equilibrium which attains the maximum social welfare. In addition, we show that future information will influence current decisions on power delivery by studying a special kind of causal allocation policy. Finally, we propose two tractable integer programs, namely the optimal arbitrage and the minimum-cost allocation problems, which can be embedded in a two-level hierarchical real-time implementation of differentiated energy services.

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“…These trends create both a need and an opportunity for dynamic pricing and demand response to help balance the power system. In a smart grid environment, price-responsive customers and devices can reschedule electricity loads from the times when electricity supply is scarce and production costs are high to times when supply is abundant and costs are low, thereby reducing bills and also improving the supply-demand balance for the power system as a whole [1][2][3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Control of Air Conditioning Improves Cost, Comfort and System Energy Balance

Izawa¹,

Fripp²

2018

Preprint

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A new model predictive control (MPC) algorithm is used to select optimal air conditioning setpoints for a commercial office building, considering variable electricity prices, weather, occupancy and lighting. This algorithm, Cost-Comfort Particle Swarm Optimization (CCPSO), is the first to combine a realistic, smooth representation of occupants' willingness to pay for thermal comfort with a bottom-up, non-linear model of the building and air conditioning system under control. We find that using a quadratic preference function for temperature can yield solutions that are both more comfortable and lower-cost than previous work that used a "brick wall" preference function with no preference for further cooling within an allowed temperature band and infinite aversion to going outside the allowed band. Using historical pricing data for a summer month in Chicago, CCPSO provided a 3% reduction in costs vs. a "brick-wall" MPC approach with similar comfort and 13% reduction in costs vs. a standard night setback strategy. CCPSO also reduced peak-hours demand by 3% vs. the "brick-wall" strategy and 15% vs. standard night-setback. At the same time, the CCPSO strategy increased off-peak energy consumption by 15% vs. the "brick-wall" strategy. This may be valuable for power systems integrating large amounts of renewable power, which can otherwise become uneconomic due to saturation of demand during off-peak hours. Keywords: HVAC model predictive control, demand response, EnergyPlus, particle swarm optimization (PSO), renewable energy, smart grids MSC: 49M37, 65K05, 90-04, 90B35, 90B50, 90C29, 90C56, 90C90, 91B08, 91B10, 91B26, 91B42, 91B74

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A Stochastic Decision-Making Model for an Electricity Retailer With Intermittent Renewable Energy and Short-Term Demand Response

Cited by 74 publications

References 29 publications

A Power Exchange Strategy for Multiple Areas with Hydro Power and Flexible Loads

A Power Exchange Strategy for Multiple Areas with Hydro Power and Flexible Loads

Market implementation of multiple-arrival multiple-deadline differentiated energy services

Multi-Objective Control of Air Conditioning Improves Cost, Comfort and System Energy Balance

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