Combined HVAC and Battery Scheduling for Demand Response in a Building

Vedullapalli, Divya T.; Hadidi, Ramtin; Schroeder, Bill

doi:10.1109/tia.2019.2938481

Cited by 60 publications

(26 citation statements)

References 12 publications

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“…Using this relaxed model in linear programming (LP) problem preserves the linear structure of the optimization problem. Thus, to reduce the computational complexity, we employ the following relaxed BESS model ( 1)- (5).…”

Section: ) Contract With Dr-capable Local Energy Resources (Lers)mentioning

confidence: 99%

“…Despite significant efforts in harnessing DR resources, a vast majority of engagement from proactive customers remains untapped [1]. An important enabler for encouraging demandside participation is to allow DR resources to participate in the wholesale market price-formation and load-dispatch process [2]- [5]. However, it is impractical to expect active market participation from individual customers.…”

Section: Introductionmentioning

confidence: 99%

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Demand-Side Participation via Economic Bidding of Responsive Loads and Local Energy Resources

Ostadijafari

Jha

Dubey

2021

IEEE Open J. Power Energy

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The active participation of demand response (DR) resources into the wholesale market price formation and load dispatch process has the potential to stimulate demand-side flexibility. However, it is challenging for a market entity to utilize the DR resources for practical use. This is because day-ahead wholesale market-clearing prices are uncertain, and DR resources are heterogeneous. Furthermore, DR participation may lead to violations of the distribution system's operational constraints. In this article, we propose an approach for an aggregator/load-serving entity (LSE) to profitably bid aggregated DR resources into the day-ahead wholesale market. The LSE requires an optimal bidding strategy that reflects the price elasticity of the aggregated retail loads to participate in the wholesale market operations. In the proposed approach, the LSE executes load curtailment and load shifting contracts with DR resources, where DR resources are remunerated for their participation at pre-contracted incentive prices. Then, the LSE aggregates the DR flexibility and optimally bids it in the day-ahead wholesale market. The proposed approach is validated using the IEEE-123-bus test system. It is demonstrated that the LSE can successfully generate economic bids for its participation in the day-ahead market by optimal management of DR resources and without violating the network's operating constraints.

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Section: ) Contract With Dr-capable Local Energy Resources (Lers)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Demand-Side Participation via Economic Bidding of Responsive Loads and Local Energy Resources

Ostadijafari

Jha

Dubey

2021

IEEE Open J. Power Energy

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“…Dynamic tariffs for energy are also included in various studies [33] to accommodate that emerging scheme. Using a storage system as a means of service for the system operator is addressed in [34] to support business opportunities for ancillary service market participants.…”

Section: Scheduling and Optimizationmentioning

confidence: 99%

Hybrid Forecast and Control Chain for Operation of Flexibility Assets in Micro-Grids

et al. 2021

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Studies on forecasting and optimal exploitation of renewable resources (especially within microgrids) were already introduced in the past. However, in several research papers, the constraints regarding integration within real applications were relaxed, i.e., this kind of research provides impractical solutions, although they are very complex. In this paper, the computational components (such as photovoltaic and load forecasting, and resource scheduling and optimization) are brought together into a practical implementation, introducing an automated system through a chain of independent services aiming to allow forecasting, optimization, and control. Encountered challenges may provide a valuable indication to make ground with this design, especially in cases for which the trade-off between sophistication and available resources should be rather considered. The research work was conducted to identify the requirements for controlling a set of flexibility assets—namely, electrochemical battery storage system and electric car charging station—for a semicommercial use-case by minimizing the operational energy costs for the microgrid considering static and dynamic parameters of the assets.

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“…Statistically, buildings account for more than 40% of the global energy consumption, of which about 40% is consumed for air conditioning [10]. Therefore, most studies on energy management for electricity customers have focused on the cost reduction via optimal air‐conditioning activity scheduling [11‐19]. However, research on optimal strategies for air‐conditioning loads under TOU and demand pricing plan are very limited.…”

Section: Introductionmentioning

confidence: 99%

Day‐ahead optimal strategy for commercial air‐conditioning load under time‐of‐use and demand pricing plan

Zhu¹,

Wang²,

Zheng

et al. 2021

Int Trans Electr Energ Syst

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Summary We study the optimal operation of air‐conditioning loads for commercial consumers based on the time‐of‐use (TOU) ($/kWh) and demand ($/kW) pricing plan. The problem is formulated as a dynamic program that aims to adjust consumer needs for electricity charges and comfort flexibly. Moreover, the Monte Carlo method is used to imitate the environmental uncertainties due to predicted outdoor temperatures. To determine the day‐ahead temperature schedules for air‐conditioning loads, a quantum‐behaved particle swarm optimization based on space compression strategy (QPSO‐SC) is presented in this paper. Space compression and particle re‐initialization by chaotic initialization enhance the performance of the QPSO‐SC. Finally, we test the proposed method for the TOU and demand pricing plan from Duke Energy. Thermostatical, daily energy charge saving, and monthly charge saving strategies are first compared, and the QPSO‐SC is further compared with the particle swarm optimization, genetic algorithm, differential evolution, and other optimization algorithms. From the extensive simulations, the QPSO‐SC is observed to be capable of yielding higher‐quality solutions stably and efficiently than the other optimization algorithms; the proposed optimal strategy can also achieve a better balance between electricity charge and customer comfort, considering environmental uncertainties.

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Combined HVAC and Battery Scheduling for Demand Response in a Building

Cited by 60 publications

References 12 publications

Demand-Side Participation via Economic Bidding of Responsive Loads and Local Energy Resources

Demand-Side Participation via Economic Bidding of Responsive Loads and Local Energy Resources

Hybrid Forecast and Control Chain for Operation of Flexibility Assets in Micro-Grids

Day‐ahead optimal strategy for commercial air‐conditioning load under time‐of‐use and demand pricing plan

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