Sharing renewable energy in smart microgrids

Zhu, Ting; Huang, Zhichuan; Sharma, Ankur; Su, Jikui; Irwin, David; Mishra, Aditya; Menasché, Daniel Sadoc; Shenoy, Prashant

doi:10.1145/2502524.2502554

Cited by 98 publications

(40 citation statements)

References 27 publications

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“…A sustained growth in adoption of energy sharing/trading in a transactive manner can effectively improve network efficiency [11], because energy is used up at or close to the point of production, which drastically reduces distance-related transmission losses. The rise in the use of DERs and ESSs enables diverse distributed generation of energy, which reduces dependency on the main grid; thus enabling the utility companies to provide other ancillary services, thereby improving the power grid efficiency.…”

Section: Improved System Efficiencymentioning

confidence: 99%

State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System

et al. 2017

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Abstract:Transaction-based energy (TE) management and control has become an increasingly relevant topic, attracting considerable attention from industry and the research community alike. As a result, new techniques are emerging for its development and actualization. This paper presents a comprehensive review of TE involving peer-to-peer (P2P) energy trading and also covering the concept, enabling technologies, frameworks, active research efforts and the prospects of TE. The formulation of a common approach for TE management modelling is challenging given the diversity of circumstances of prosumers in terms of capacity, profiles and objectives. This has resulted in divergent opinions in the literature. The idea of this paper is therefore to explore these viewpoints and provide some perspectives on this burgeoning topic on P2P TE systems. This study identified that most of the techniques in the literature exclusively formulate energy trade problems as a game, an optimization problem or a variational inequality problem. It was also observed that none of the existing works has considered a unified messaging framework. This is a potential area for further investigation.

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Section: Improved System Efficiencymentioning

confidence: 99%

State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System

et al. 2017

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“…For example, Figure 4 shows that even on a relatively sunny day, the energy generated by this 10kW home solar installation rises and falls dramatically based on passing clouds. In addition, future microgrids, which promise to increase grid reliability by leveraging local energy sources, will benefit much less from aggregation effects [22]. Finally, the potential benefits of incentivizing all consumers to adopt advanced load scheduling, rather than none of them (which is effectively the case today), likely outweighs the cost of over-optimization.…”

Section: Discussionmentioning

confidence: 99%

“…This freedom includes the option to transparently shift, slide, stretch, store, or sell power for some loads without consumer involvement. For example, a scheduling algorithm may partially i) shift a thermostatic or timer-driven load's duty cycle, ii) slide a batched load's start time into the future, iii) stretch an elastic load's operation to reduce its peak usage, iv) store power in a battery to alter a load's profile, or v) sell power produced by renewables back to the grid or other buildings, e.g., in a microgrid [22]. Of course, each dimension of scheduling freedom has inherent limitations.…”

Section: Introductionmentioning

confidence: 99%

Incentivizing Advanced Load Scheduling in Smart Homes

Irwin

Shenoy

2013

Proceedings of the 5th ACM Workshop on Embedded Systems for Energy-Efficient Buildings

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In recent years, researchers have proposed numerous advanced load scheduling algorithms for smart homes with the goal of reducing the grid's peak power usage. In parallel, utilities have introduced variable rate pricing plans to incentivize residential consumers to shift more of their power usage to low-price, off-peak periods, also with the goal of reducing the grid's peak power usage. In this paper, we argue that variable rate pricing plans do not incentivize consumers to adopt advanced load scheduling algorithms. While beneficial to the grid, these algorithms do not significantly lower a consumer's electric bill. To address the problem, we propose flat-power pricing, which directly incentivizes consumers to flatten their own demand profile, rather than shift as much of their power usage as possible to low-cost, off-peak periods. Since most loads have only limited scheduling freedom, load scheduling algorithms often cannot shift much, if any, power usage to low-cost, off-peak periods, which are often many hours in the future. In contrast, flat-power pricing encourages consumers to shift power usage even over short time intervals to flatten demand. We evaluate the benefits of advanced load scheduling algorithms using flat-power pricing, showing that consumers save up to 40% on their electric bill, compared with 11% using an existing time-of-use rate plan.

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“…Storage and efficient utilization of renewable energy can be helped through coordinating multiple smart homes and microgrids together to pool their storage, load, and generation capacities. These smart homes can coordinate their loads to have the least impact on the bulk electric power system [10] [11] [12].…”

Section: Introductionmentioning

confidence: 99%

Application of power sensors in the control and monitoring of a residential microgrid

Diefenderfer

Jansson

Prescott

2015

2015 IEEE Sensors Applications Symposium (SAS)

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Advancements in sensor and metering technologies enable us to affordably collect more data than ever before and this data can be used to revolutionize the power industry. The ability to sense and monitor power characteristics in near real time and feed this information to control systems operating to optimize the grid enables better operation of both large and small scale power systems. Real-time power sensors working with a near real-time control system is necessary for a microgrid which has intermittent renewable generation and time varying loads. Knowing how much energy is flowing through the system is crucial for stable operation while islanded and is important to maintain economic feasibility. The application of advancements in cloud communications and embedded sensors can be used to augment the control of a residential microgrid through the real time data collection and control of both loads and generation resources. Using an advanced sub-metering system to monitor the flow of energy through the microgrid, a connection to the ISO's energy market, and local environmental data provides efficient operation and insight into the near real-time operation of the system. The data can also be logged for future analysis external to the system to allow for better future development of control strategies and can be used to make better predictions of energy needs and costs. These predictions can be used to ensure the renewable energy is used in the most economic manner ensuring the best possible equivalent price of energy. Such an integrated and predictive system can also store thermal energy inaugurating a paradigm shift from a "demand response market" to one that becomes an "availability responsive market".

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Sharing renewable energy in smart microgrids

Cited by 98 publications

References 27 publications

State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System

State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System

Incentivizing Advanced Load Scheduling in Smart Homes

Application of power sensors in the control and monitoring of a residential microgrid

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