Provision of regulation service reserves by flexible distributed loads

Caramanis, Michael C.; Paschalidis, Ioannis Ch.; Cassandras, Christos G.; Bilgin, Enes; Ntakou, Elli

doi:10.1109/cdc.2012.6426025

Cited by 18 publications

(9 citation statements)

References 10 publications

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“…There is an electrical vehicle (EV) charging station at the southeastern corner of each residential quarter. Following [10], at time t, inside each building ni(t) appliances are consuming actively with a rate ri kW with subscripts c, r and e denoting commercial building, residential building and EV station, respectively. The arrival distribution of appliances for building class i over the period [t, t+1] is Poisson distributed with a rate λi(Ui −pj(t))/Ui, where Ui is the utility of an appliance of class i and pj(t) is the broadcast price for neighborhood class j, j ∈ {c, r} with residential building and EV station charged by the same price.…”

Section: Smart Neighborhood Power Managementmentioning

confidence: 99%

SpaTeL

Haghighi

Jones

Kong

et al. 2015

Proceedings of the 18th International Conference on Hybrid Systems: Computation and Control

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Networked dynamical systems are increasingly used as models for a variety of processes ranging from robotic teams to collections of genetically engineered living cells. As the complexity of these systems increases, so does the range of emergent properties that they exhibit. In this work, we define a new logic called Spatial-Temporal Logic (SpaTeL) that is a unification of signal temporal logic (STL) and tree spatial superposition logic (TSSL). SpaTeL is capable of describing high-level spatial patterns that change over time, e.g., "Power consumption in the northwest quadrant of the city drops below 100 megawatts if the power consumption in the southwest quadrant remains above 200 megawatts for two hours." We present a statistical model checking procedure that evaluates the probability with which a networked system satisfies a SpaTeL formula. We also develop a synthesis procedure that determines system parameters maximizing the average degree of satisfaction, a continuous measure that quantifies how strongly a system execution satisfies a given formula. We demonstrate our algorithms on two systems: a biochemical reaction-diffusion system and a demand-side management system for a smart neighborhood.

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Section: Smart Neighborhood Power Managementmentioning

confidence: 99%

SpaTeL

Haghighi

Jones

Kong

et al. 2015

Proceedings of the 18th International Conference on Hybrid Systems: Computation and Control

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“…Once P and R are set, each vehicle adjusts its charging power in the next hour based on the RS signal z(t) which is dynamically broadcasted from the power grid. The RS signal z(t) is generated based on the real-time power market situation and is used to balance the supply and demand in the power market [22]. For each individual PEV owner who has very little effect on the entire power market, z(t) can be considered as a given signal.…”

Section: Model Of Regulation Service Provisioning Marketmentioning

confidence: 99%

Optimal control of PEVs for energy cost minimization and frequency regulation in the smart grid accounting for battery state-of-health degradation

Cui

Wang

Chen

et al. 2015

Proceedings of the 52nd Annual Design Automation Conference

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Plug-in electric vehicles (PEVs) are considered the key to reducing the fossil fuel consumption and an important part of the smart grid. The plug-in electric vehicle-to-grid (V2G) technology in the smart grid infrastructure enables energy flow from PEV batteries to the power grid so that the grid stability is enhanced and the peak power demand is shaped. PEV owners will also benefit from V2G technology as they will be able to reduce energy cost through proper PEV charging and discharging scheduling. Moreover, power regulation service (RS) reserves have been playing an increasingly important role in modern power markets. It has been shown that by providing RS reserves, the power grid achieves a better match between energy supply and demand in presence of volatile and intermittent renewable energy generation. This paper addresses the problem of PEV charging under dynamic energy pricing, properly taking into account the degradation of battery state-of-health (SoH) during V2G operations as well as RS provisioning. An overall optimization throughout the whole parking period is proposed for the PEV and an adaptive control framework is presented to dynamically update the optimal charging/discharging decision at each time slot to mitigate the effect of RS tracking error. Experimental results show that the proposed optimal PEV charging algorithm minimizes the combination of electricity cost and battery aging cost in the RS provisioning power market.

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“…Many authors have studied the impacts of demand flexibility in various contexts (Caramanis & Foster, 2009;Caramanis, Paschalidis, Cassandras, Bilgin, & Ntakou, 2012;Kefayati & Caramanis, 2010;Mohsenian-Rad & LeonGarcia, 2010;Papavasiliou & Oren, 2010;Turitsyn, Sinitsyn, Backhaus, & Chertkov, 2010;Turitsyn, Backhaus, Ananyev, & Chertkov, 2011) and demonstrated the potential benefits of demand flexibility. Considering a residential setting, Mohsenian-Rad and Leon-Garcia (2010) propose a price-based load scheduling algorithm for smart grids.…”

Section: Introductionmentioning

confidence: 99%

Optimal policies for simultaneous energy consumption and ancillary service provision for flexible loads under stochastic prices and no capacity reservation constraint

Kefayati

Baldick

2015

International Journal of Control

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Flexible loads, i.e. the loads whose power trajectory is not bound to a specific one, constitute a sizable portion of current and future electric demand. This flexibility can be used to improve the performance of the grid, should the right incentives be in place. In this paper, we consider the optimal decision making problem faced by a flexible load, demanding a certain amount of energy over its availability period, subject to rate constraints. The load is also capable of providing ancillary services (AS) by decreasing or increasing its consumption in response to signals from the independent system operator (ISO). Under arbitrarily distributed and correlated Markovian energy and AS prices, we obtain the optimal policy for minimising expected total cost, which includes cost of energy and benefits from AS provision, assuming no capacity reservation requirement for AS provision. We also prove that the optimal policy has a multi-threshold form and can be computed, stored and operated efficiently. We further study the effectiveness of our proposed optimal policy and its impact on the grid. We show that, while optimal simultaneous consumption and AS provision under real-time stochastic prices are achievable with acceptable computational burden, the impact of adopting such real-time pricing schemes on the network might not be as good as suggested by the majority of the existing literature. In fact, we show that such price responsive loads are likely to induce peak-to-average ratios much more than what is observed in the current distribution networks and adversely affect the grid. IntroductionSmart grids are expected to bring about fundamental changes in terms of information availability to electricity networks and provide bidirectional communication and energy exchange even to the end points of the distribution network. Although it is generally understood that the closed loop of information/energy exchange can improve the overall energy exchange process in many ways (e.g. reducing cost, increasing reliability), models to quantitatively understand the fundamental benefits of the information availability over smart grids are relatively lacking.A considerable portion of the current electricity demand is inherently flexible, i.e. electric power need not be delivered to the load at a very specific trajectory over time. For these loads, an amount of energy is needed by some (potentially recurring) deadline. In other words, over the operation time of the load, the trajectory of the delivered power only needs to satisfy some constraints instead of exactly following a specific trajectory. Such loads include most heating/cooling systems and electric vehicles (EV). The most recent EIA statistics (U.S. Energy Information Administration (EIA), 2009) suggest that such loads comprise more than 50% of average res-

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Provision of regulation service reserves by flexible distributed loads

Cited by 18 publications

References 10 publications

SpaTeL

SpaTeL

Optimal control of PEVs for energy cost minimization and frequency regulation in the smart grid accounting for battery state-of-health degradation

Optimal policies for simultaneous energy consumption and ancillary service provision for flexible loads under stochastic prices and no capacity reservation constraint

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