Incorporating Demand Response With Spinning Reserve to Realize an Adaptive Frequency Restoration Plan for System Contingencies

Chang-Chien, Le-Ren; An, Luu Ngoc; Lin, Tsung Yi; Lee, Wei-Jen

doi:10.1109/tsg.2012.2192297

Cited by 82 publications

(43 citation statements)

References 14 publications

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“…For a supposed contingency (e.g., 10% generation loss or the frailer of the largest generator), the frequency nadir should be above ∆f safe [8]. The system frequency limit equation is defined in Equation (11).…”

Section: Day-ahead Scheduling With Flexible Demand Response (Dr) Resomentioning

confidence: 99%

“…During recent years, increasing attention have been paid to DR in the frequency control. A variety of control strategies, e.g., decentralized control strategies [6,7], centralized control strategies [8,9], hybrid hierarchical control strategies [10], proportional-integral (PI) control method [11,12], linear quadratic regulator method [13], H ∞ control method [14], etc., have been developed. Some researches incorporate DR with other resources in the frequency control, e.g., wind generators [15], storages [16], etc.…”

Section: Introductionmentioning

confidence: 99%

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Day-Ahead Scheduling Considering Demand Response as a Frequency Control Resource

Bao

Wang

et al. 2017

Energies

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Abstract:The development of advanced metering technologies makes demand response (DR) able to provide fast response services, e.g., primary frequency control. It is recognized that DR can contribute to the primary frequency control like thermal generators. This paper proposes a day-ahead scheduling method that considers DR as a frequency control resource, so that the DR resources can be dispatched properly with other resources. In the proposed method, the objective of frequency control is realized by defining a frequency limit equation under a supposed contingency. The frequency response model is used to model the dynamics of system frequency. The nonlinear frequency limit equation is transformed to a linear arithmetic equation by piecewise linearization, so that the problem can be solved by mixed integer linear programming (MILP). Finally, the proposed method is verified on numerical examples.

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Section: Day-ahead Scheduling With Flexible Demand Response (Dr) Resomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Day-Ahead Scheduling Considering Demand Response as a Frequency Control Resource

Bao

Wang

et al. 2017

Energies

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“…DR can also be achieved by alternative approaches, such as aggregated load control [14,15], plug-in electric vehicles [16] and frequency-related control [17]. Pourmousavi et al evaluated the thermostat setpoint control of aggregate electric water heaters for load shifting, and for providing a desired balancing reserve for the utility.…”

Section: Introductionmentioning

confidence: 99%

“…The DR program was used first to restore declining frequency caused by a large disturbance. The scheduled spinning reserve was then used to raise frequency back to the pre-disturbance level [17].…”

Section: Introductionmentioning

confidence: 99%

Novel Genetic Algorithm-Based Energy Management in a Factory Power System Considering Uncertain Photovoltaic Energies

Hong

2017

Applied Sciences

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Abstract:The demand response and accommodation of different renewable energy resources are essential factors in a modern smart microgrid. This paper investigates the energy management related to the short-term (24 h) unit commitment and demand response in a factory power system with uncertain photovoltaic power generation. Elastic loads may be activated subject to their operation constraints in a manner determined by the electricity prices while inelastic loads are inflexibly fixed in each hour. The generation of power from photovoltaic arrays is modeled as a Gaussian distribution owing to its uncertainty. This problem is formulated as a stochastic mixed-integer optimization problem and solved using two levels of algorithms: the master level determines the optimal states of the units (e.g., micro-turbine generators) and elastic loads; and the slave level concerns optimal real power scheduling and power purchase/sale from/to the utility, subject to system operating constraints. This paper proposes two novel encoding schemes used in genetic algorithms on the master level; the point estimate method, incorporating the interior point algorithm, is used on the slave level. Various scenarios in a 30-bus factory power system are studied to reveal the applicability of the proposed method.

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“…In contrast, in the environment of a smart grid, the increasing employment of smart meters, intelligent appliances, and other intelligent terminal equipment provides abundant dynamic information to secure and stabilize the power grid, thereby enabling the power loads to actively participate in more advanced applications (especially in load shifting [6,7] and frequency regulation [8][9][10][11][12]) in response to a price signal or an event trigger. Because all of the existing applications of active load control are performed over a relatively long time scale (at least at the minute level), there is widespread interest discovering whether these power loads can actively participate in handling the emergency situations of a power system, such as the frequency stability issue at the second or millisecond level.…”

Section: Introductionmentioning

confidence: 99%

Coordinated Scheme of Under-Frequency Load Shedding with Intelligent Appliances in a Cyber Physical Power System

et al. 2016

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Abstract:The construction of a cyber physical system in a power grid provides more potential control strategies for the power grid. With the rapid employment of intelligent terminal equipment (e.g., smart meters and intelligent appliances) in the environment of a smart grid, abundant dynamic response information could be introduced to support a secure and stable power system. Combining demand response technology with the traditional under-frequency load shedding (UFLS) scheme, a new UFLS strategy-determining method involving intelligent appliances is put forward to achieve the coordinated control of quick response resources and the traditional control resources. Based on this method, intelligent appliances can be used to meet the regulatory requirements of system operation in advance and prevent significant frequency drop, thereby improving the flexibility and stability of the system. Time-domain simulation verifies the effectiveness of the scheme, which is able to mitigate frequency drop and reduce the amount of load shedding.

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Incorporating Demand Response With Spinning Reserve to Realize an Adaptive Frequency Restoration Plan for System Contingencies

Cited by 82 publications

References 14 publications

Day-Ahead Scheduling Considering Demand Response as a Frequency Control Resource

Day-Ahead Scheduling Considering Demand Response as a Frequency Control Resource

Novel Genetic Algorithm-Based Energy Management in a Factory Power System Considering Uncertain Photovoltaic Energies

Coordinated Scheme of Under-Frequency Load Shedding with Intelligent Appliances in a Cyber Physical Power System

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