Review and prospect of integrated demand response in the multi-energy system

Wang, Jianxiao; Zhong, Haiwang; Ma, Ziming; Xia, Qing; Chen, Kang

doi:10.1016/j.apenergy.2017.05.150

Cited by 414 publications

(152 citation statements)

References 67 publications

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“…F R means cost for unutilized reserve capacity. At the end, the demand response cost, A D , can be supposed according to (12):…”

Section: Effective Electricity Cost On Annual Basismentioning

confidence: 99%

“…Demand response is commonly observed as a cheaper alternative than rapid response production reserves, which reduces the cost of depleting expensive production resources, though magnitude, duration, and speed of response are important aspects. [12][13][14][15] However, the strategy of load control can be partially complex for the demand response, because it competes with local and global targets and partly because of design and modeling complexity of the delivery control itself. 16 Digital modeling of high resource scheduling difficulties is hard to employ, and the model of demand response generally does not include most important characteristics of load needed for decisions of optimal resource allocation.…”

mentioning

confidence: 99%

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Optimal asset allocation of wind energy units in conjunction with demand response for a large‐scale electric grid

et al. 2019

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Summary Demand response is considered to be a realistic and comparatively inexpensive solution aimed at increasing the penetration of renewable generations into the bulk electricity systems. The work in this paper highlights the demand response in conjunction with the optimal capacity of installed wind energy resources allocation. Authors proposed a total annual system cost model to minimize the cost of allocating wind power generating assets. This model contains capacity expansion, production, uncertainty, wind variability, emissions, and elasticity in demand to find out cost per hour to deliver electricity. A large‐scale electric grid (25 GW) is used to apply this model. Authors discovered that demand response based on interhourly system is not as much helpful as demand response grounded on intrahourly system. According to results, 32% wind generation share will provide the least cost. It is also worth noting that optimal amount of wind generation is much sensitive to installation cost as well as carbon tax.

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“…F R means cost for unutilized reserve capacity. At the end, the demand response cost, A D , can be supposed according to (12):…”

Section: Effective Electricity Cost On Annual Basismentioning

confidence: 99%

mentioning

confidence: 99%

Optimal asset allocation of wind energy units in conjunction with demand response for a large‐scale electric grid

et al. 2019

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“…∆P CCHP is the power of increased electricity generation of CCHP. P i,j is the power of device j in factory i. Equations (21) and (22) denote the increased electricity generation of CCHP by heating and cooling demand responses, respectively. Equation (23) denotes the performance coefficient of electricity heating or cooling.…”

Section: Model Of Heating and Cooling Demand Responsementioning

confidence: 99%

Bilevel Optimal Dispatch Strategy for a Multi-Energy System of Industrial Parks by Considering Integrated Demand Response

Zhao

Peng

et al. 2018

Energies

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To combat energy shortage, the multi-energy system has gained increasing interest in contemporary society. In order to fully utilize adjustable multi-energy resources on the demand side and reduce interactive compensation, this paper presents an integrated demand response (IDR) model in consideration of conventional load-shedding and novel resource-shifting, due to the fact that participants in IDR can use more abundant resources to reduce the consumption of energy. In the proposed IDR, cooling, heating, electricity, gas and so forth are considered, which takes the connection between compensation and load reductions into consideration. Furthermore, a bilevel optimal dispatch strategy is proposed to decrease the difficulty in coordinated control and interaction between lower-level factories and upper-level multi-energy operators in industrial parks. In this strategy, resources in both multi-energy operator and user sides are optimally controlled and scheduled to maximize the benefits under peak shifting constraint. In the normal operation mode, this strategy can maximize the benefits to users and multi-energy operators. Particularly in heavy load conditions, compared to the conventional electricity demand response, there are more types of adjustable resources, more flexibility, and lower interactive compensations in IDR. The results indicate that optimal operation for factories and multi-energy operators can be achieved under peak shifting constraint and the overall peak power value in industrial park is reduced.

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“…Electricity sold through the public utility network kW M 6 Carbon dioxide released from storage kg/h M 9…”

Section: Decision Variables and Constraintsmentioning

confidence: 99%

“…On the one hand, recent concepts such as distributed multi-generation (DMG) [4] and multi-energy systems (MES) [5] have come to set a general research framework to manage systems that include various energy carriers and integrating them in demand response programs [6]. Within the so-called MES, the energy hub approach is widely used as a simplified model for the interactions inside diverse complexity systems relating to their input-output structure.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous resource management in energy hubs with self-consumption: Contributions and application example

et al. 2018

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The energy hub concept and modeling methodology are widely employed tools for solving resource conversion and storage scheduling problems. For instance, industrial clusters might benefit from determining the suitable time to operate their facilities and to sell electricity to the public power grid, according to legal, economic or environmental factors. In this paper, novel elements are introduced in order to more accurately represent real plants and to reduce the amount of decision variables. The major innovation is to consider devices consuming a resource which is not related to the quantity of output produced, by attaching binary decision variables to certain energy hub outputs. Secondly, a path vector is defined to take into account the flows of resources within the system instead of employing a variable for each branch between the components. The third innovation consists of an additional vector to express the amount of output resources sold from the energy hub, including constraints for those resources which are exported and imported through the same medium. An extended energy hub model is first proposed and then applied to a real plant example, including multiple and heterogeneous resources and performing a comparison between days with different demands, weather conditions and electricity prices. The results obtained in the selected scenarios demonstrate a logical operation scheduling, and therefore validate the proposed approach.

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Review and prospect of integrated demand response in the multi-energy system

Cited by 414 publications

References 67 publications

Optimal asset allocation of wind energy units in conjunction with demand response for a large‐scale electric grid

Optimal asset allocation of wind energy units in conjunction with demand response for a large‐scale electric grid

Bilevel Optimal Dispatch Strategy for a Multi-Energy System of Industrial Parks by Considering Integrated Demand Response

Heterogeneous resource management in energy hubs with self-consumption: Contributions and application example

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