An experiment in real time pricing for control of electric thermal storage systems

Daryanian, B.; Bohn, Roger E.; Tabors, Richard D.

doi:10.1109/59.116976

Cited by 25 publications

(12 citation statements)

References 2 publications

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“…By including Equation (14) in the constraint of ULP, the bilevel optimization problem is composed. The constraint that is related to the range of the incentive price can be necessary for finding the incentive price within the reasonable range, because more than two incentive price solutions that determine the identical EES charging schedules and optimal value of the LLP can exist.…”

Section: Combining Upper-level Problem and Lower-level Problemmentioning

confidence: 99%

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Indirect Load Control for Energy Storage Systems Using Incentive Pricing under Time-of-Use Tariff

2016

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Indirect load control (ILC) is a method by which the customer determines load reduction of electricity by using a price signal. One of the ILCs is a time-of-use (TOU) tariff, which is the most commonly used time-varying retail pricing. Under the TOU tariff, the customer can reduce the energy cost through an energy storage system (ESS). However, because this tariff is fixed for several months, the ESS operation does not truly reflect the wholesale market price, which could widely fluctuate. To overcome this limitation, this paper proposes an incentive pricing method in which the load-serving entity (LSE) gives the incentive pricing signal to the customers with ESSs. Because the ESS charging schedule is determined by the customer through ILC, a bilevel optimization problem that includes the customer optimization problem is utilized to determine the incentive pricing signal. Further, the bilevel optimization problem is reformulated into a one-level problem to be solved by an interior point method. In the proposed incentive scheme: (1) the social welfare increases and (2) the increased social welfare can be equitably divided between the LSE and the customer; and (3) the proposed incentive scheme leads the customer to voluntarily follow the pricing signal.

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Section: Combining Upper-level Problem and Lower-level Problemmentioning

confidence: 99%

“…Therefore, the proposed bilevel problem is the problem to solve Equation (4) subject to Equations (5), (14) and (15).…”

Section: Combining Upper-level Problem and Lower-level Problemmentioning

confidence: 99%

Indirect Load Control for Energy Storage Systems Using Incentive Pricing under Time-of-Use Tariff

2016

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“…municipal water plants); [7] reports on an experiment on the real-time pricing based control of thermal storage to save cost. However, [6][7] did not consider day-ahead market, but only consider real-time market.…”

Section: Introductionmentioning

confidence: 99%

Optimal scheduling and operation of load aggregators with electric energy storage facing price and demand uncertainties

Xie

Singh

2011

2011 North American Power Symposium

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In competitive power markets, with increasing penetration of variable renewable energy resources such as wind power, electricity price becomes more uncertain. In distribution systems, adoption of renewable distributed generation technologies adds another dimension of uncertainty in load forecast. Facing these higher price and load uncertainties, it becomes more challenging for load aggregators to manage their electricity cost. Within this context, this paper presents a Model Predictive Control (MPC)-based scheduling and operation strategy for the load aggregator with electric energy storage (EES) to manage electricity cost in day-ahead and real-time power markets with different levels of price and load uncertainties. Price and load forecasts are actively integrated into the scheduling and operation decision making process to determine the optimal operation. Two other strategies are also discussed and studied for comparison. Case studies demonstrate better performance of the proposed MPC-based strategy compared to the other two strategies facing different levels of price and load uncertainties. The MPC-based strategy is also shown to be robust with the increase of price and load uncertainties. The benefit of energy arbitrage with MPC-based strategy is also illustrated. Index Terms--Electricitycost management, Electric energy storage, Power market, Load aggregator, Model Predictive Control, Price and load uncertainties. Yixing Xu (S'08) is currently pursuing Ph.D. degree at Texas A&M University. He received his B.E. in Electrical Engineering in 2007 from Tsinghua University, Beijing, China. His research interest includes power systems reliability evaluation, integration of energy storage systems and renewable energy in the power systems, smart grid and electricity market. His industry experience includes an internship (

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“…An early work that takes advantage of thermal storage and real time pricing to save electricity bill in commercial buildings is [11]. The work considers the buildings as a whole.…”

Section: Related Workmentioning

confidence: 99%

“…We can also get the state coefficients a, b, c, d by regression analysis of data of electricity charging and thermal demand from EnergyPlus [11][37]. For energy consumption of the rooms, the number of parameters to be used for EnergyPlus increases fast as rooms are very different.…”

Section: ) Modeling Thermal Storage and Roomsmentioning

confidence: 99%

Minimizing Building Electricity Costs in a Dynamic Power Market: Algorithms and Impact on Energy Conservation

Pan

Wang

Cao

et al. 2013

2013 IEEE 34th Real-Time Systems Symposium

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Abstract-Energy is a global concern and the electricity bills nowadays are leading to unprecedented costs. Electricity price is market-based and dynamic. In this paper, we investigate how to cut the electricity bills of commercial buildings in a dynamic power market. The building thermal systems (e.g., air-conditioning), which dominate electricity bills, has a special property of thermal storage, i.e., the energy will not immediately dissipate from thermal air/water. Intuitively, with storage, the energy can be "stored" in the thermal system, making it possible to purchase electricity in low price and use it at appropriate time. The building thermal supply and electricity purchasing surely depends on human activities that the building should support such as class and meeting schedules. To minimize electricity bills, we develop a holistic planning of electricity purchasing schedule with thermal storage management, and appropriate room assignment schedules for classes/meetings usage.The computing algorithms require inputs of physical modeling on energy consumption. We develop wireless sensing systems to collect fine-grained data which are used to assist the cross-disciplinary physical modeling. We conduct validation through real experiments. We formulate an optimization problem and show that it is NP-complete. Our primary focus is to minimize electricity bills, which matches the incentives of the commercial buildings. We show that this does not coincide with energy conservation. We further investigate the relationship of minimization of electricity bills and minimization of energy consumption. We develop algorithms for our problem and our evaluation shows that we can achieve a 40% cost reduction.

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An experiment in real time pricing for control of electric thermal storage systems

Cited by 25 publications

References 2 publications

Indirect Load Control for Energy Storage Systems Using Incentive Pricing under Time-of-Use Tariff

Indirect Load Control for Energy Storage Systems Using Incentive Pricing under Time-of-Use Tariff

Optimal scheduling and operation of load aggregators with electric energy storage facing price and demand uncertainties

Minimizing Building Electricity Costs in a Dynamic Power Market: Algorithms and Impact on Energy Conservation

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