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On hourly home peak load prediction
- In IEEE International Conference on Smart Grid Communications
, 2013
"... Abstract—The Ontario electrical grid is sized to meet peak electricity load. A reduction in peak load would allow deferring large infrastructural costs of additional power plants, thereby lowering generation cost and electricity prices. Proposed solutions for peak load reduction include demand respo ..."
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Abstract—The Ontario electrical grid is sized to meet peak electricity load. A reduction in peak load would allow deferring large infrastructural costs of additional power plants, thereby lowering generation cost and electricity prices. Proposed solutions for peak load reduction include demand response and storage. Both these solutions require accurate prediction of a home’s peak and mean load. Existing work has focused only on mean load prediction. We find that these methods exhibit high error when predicting peak load. Moreover, a home’s historic peak load and occupancy is a better predictor of peak load than observable physical characteristics such as temperature and season. We explore the use of Seasonal Auto Regressive Moving Average (SARMA) for peak load prediction and find that it has 30% lower root mean square error than best known prior methods. I.
Integrating Energy Storage in Electricity Distribution Networks
"... Electricity generation combined with its transmission and distribu-tion form the majority of an electric utility’s recurring operating costs. These costs are determined, not only by the aggregate en-ergy generated, but also by the maximum instantaneous peak power demand required over time. Prior wor ..."
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Electricity generation combined with its transmission and distribu-tion form the majority of an electric utility’s recurring operating costs. These costs are determined, not only by the aggregate en-ergy generated, but also by the maximum instantaneous peak power demand required over time. Prior work proposes using energy stor-age devices to reduce these costs by periodically releasing energy to lower the electric grid’s peak demand. However, prior work gen-erally considers only a single storage technology employed at a single level of the electric grid’s hierarchy. In this paper, we ex-amine the efficacy of employing different combinations of storage technologies at different levels of the grid’s distribution hierarchy. We present an optimization framework for modeling the primary characteristics that dictate the lifetime cost of many prominent en-ergy storage technologies. Our framework captures the important tradeoffs in placing different technologies at different levels of the distribution hierarchy with the goal of minimizing a utility’s oper-ating costs. We evaluate our framework using real smart meter data from 5000 customers of a local electric utility. We show that by em-ploying hybrid storage technologies at multiple levels of the distri-bution hierarchy, utilities can reduce their daily operating costs due to distributing electricity by up to 12%.
iDES: Incentive-Driven Distributed Energy Sharing
"... Abstract—Buildings consume a significant amount of elec-tricity, which is normally generated from dirty sources causing an increase in carbon footprints. To reduce carbon footprint, distributed renewable energy generation has been proposed. How-ever, the amount of renewable energy harvested normally ..."
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Abstract—Buildings consume a significant amount of elec-tricity, which is normally generated from dirty sources causing an increase in carbon footprints. To reduce carbon footprint, distributed renewable energy generation has been proposed. How-ever, the amount of renewable energy harvested normally does not match the amount of energy consumed in individual homes. To address this mismatch, we propose a distributed solution to share renewable energy among homes, which form a microgrid. Specifically we (i) design an incentive-driven distributed energy sharing system (iDES) in a microgrid to enable effective energy sharing and reduce the communication overhead, and (ii) develop energy sharing pricing model to incentivize energy sharing. The energy sharing price generally reflects the installation costs of on-site renewable and energy storage units, the dynamic changes of energy supply-demand relationship, and the remaining energy level of batteries. We validated the effectiveness of our system with extensive evaluations that use empirical traces. The results show that our energy sharing pricing model can effectively motivate and encourage homes to share energy.
IEEE TRANSACTIONS ON SMART GRID 1 When Bias Matters: An Economic Assessment of Demand Response Baselines for Residential Customers
"... Abstract—Demand response (DR) has been known to play an important role in the electricity sector to balance supply and demand. To this end, the DR baseline is a key factor in a successful DR program since it influences the incentive allocation mechanism and customer participation. Previous studies h ..."
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Abstract—Demand response (DR) has been known to play an important role in the electricity sector to balance supply and demand. To this end, the DR baseline is a key factor in a successful DR program since it influences the incentive allocation mechanism and customer participation. Previous studies have investigated baseline accuracy and bias for large, industrial and commercial customers. However, the analysis of baseline performance for residential customers has received less attention. In this paper, we analyze DR baselines for residential customers. Our analysis goes beyond accuracy and bias by understanding the impact of baselines on all stakeholders ’ profit. Using our customer models, we successfully show how customer partic-ipation changes depending on the incentive actually received. We found that, in general, bias is more relevant than accuracy for determining which baseline provides the highest profit to stakeholders. Consequently, this result provides a valuable insight into designing effective DR incentive schemes. Index Terms—residential demand response, smart grid, demand-side management, demand response baseline, net benefit analysis. I.
978-3-901882-56-2 c©2013 IFIP Efficient Demand Assignment in Multi-Connected
"... Abstract—With the proliferation of distributed generation, an electrical load can be satisfied either by a centralized generator or by local/nearby distributed generators. Given a set of resource demands in a collection of geographically co-located microgrids connected to the central grid, each such ..."
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Abstract—With the proliferation of distributed generation, an electrical load can be satisfied either by a centralized generator or by local/nearby distributed generators. Given a set of resource demands in a collection of geographically co-located microgrids connected to the central grid, each such demand characterized by a power level and a duration. We study algorithms that allocate generation resources to demands by configuring switched paths from sources to loads. We consider the case when each demand can be met by two generators, one of them representing the central grid and thus shared among all demands. I.
Demand Response through a Temperature Setpoint Market in Ontario
"... Abstract—The electrical grid is designed to meet peak loads, which may occur for only a few hours each year. Consequently, there are significant economic gains from a reduction in the peak load. Air conditioner (AC) load from residential buildings forms a significant portion of peak summer loads. Th ..."
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Abstract—The electrical grid is designed to meet peak loads, which may occur for only a few hours each year. Consequently, there are significant economic gains from a reduction in the peak load. Air conditioner (AC) load from residential buildings forms a significant portion of peak summer loads. The existing ‘peaksaver ’ program in Ontario attempts to reduce AC loads by setting thermostats a few degrees higher in volunteer households on hot summer days. This has had only a limited success. To address this issue, we propose a scheme that provides monetary incentive for participation. We describe the operation of this ‘temperature market ’ and demonstrate its effectiveness with a heterogeneous population of potential participants. We find that even a payment of $2 per hour of setback can reduce grid operating costs by $688 million over a period of 20 years. I.
