Stochastic programming model for incentive‐based demand response considering complex uncertainties of consumers

Zheng, Shunlin; Yi, Sun; Li, Bin; Hu, Yuanman; Qi, Bing; Shi, Kun; Li, Yuanfei

doi:10.1049/iet-gtd.2020.0692

Cited by 19 publications

(6 citation statements)

References 35 publications

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“…MES-IDR extends the traditional concept of power demand response within the context of MES. Traditional demand response (DR) is defined as the alteration of electricity consumption by users during specific time periods, guided by electricity prices [3,4] or incentives [5,6], to achieve a balance between energy supply and demand [7]. Conversely, MES-IDR is induced through pricing or incentives to influence the demand for one or multiple forms of energy, consequently impacting the demand for other types of energy.…”

Section: Background and Motivationmentioning

confidence: 99%

A collaborative training approach for multi energy systems in low‐carbon parks accounting for response characteristics

Bao,

Sun,

Zheng

2023

IET Renewable Power Gen

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Existing researches lack systematic design of the operation mechanism and overall implementation process of park oriented multi‐state energy system; The existing researches on multi‐state energy systems ignored the dynamic response characteristics of load and the coupling effect of units considering the impact of carbon emissions, and did not go deep into the collaborative management of multiple multi state energy system parks. To this end, this paper first proposes a two‐stage multi‐energy system operation architecture for low carbon parks, and establishes a mathematical model considering carbon emissions, operating costs and other objectives. The response space of load is optimized by considering the dynamic interaction characteristics of load response and the coupling characteristics of unit operation. Then, considering the problems of data security and training efficiency, F‐DDPG technology is used to conduct collaborative training for multiple campus systems. Finally, an example is given to prove the effectiveness of the method, which is not only conducive to reducing the operating cost of the system and the carbon emissions on the supply side, but also improves the convergence of model training. In the case of collaborative training in multiple parks, the method in this paper converges faster and has better economic benefits.

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Section: Background and Motivationmentioning

confidence: 99%

A collaborative training approach for multi energy systems in low‐carbon parks accounting for response characteristics

Bao,

Sun,

Zheng

2023

IET Renewable Power Gen

Self Cite

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show abstract

“…For the thermal storage tank, the energy is composed of the temperature change, energy loss, the energy from solar collector, make-up water, the energy to the supplying tank and the energy for selling, its model is described as (18).…”

Section: Fig2 Spts Structure Diagrammentioning

confidence: 99%

“…At present, stochastic optimization, robust optimization and chanceconstraint programming have been proposed to solve it. Stochastic programming generates discrete various scenes based on probability density, then selects the typical scenes through scene reduction [18]. Heavy computational burden caused by a large number of detailed scenarios is inevitable in stochastic optimization, and it is difficult to select the typical scenes with representativeness.…”

mentioning

confidence: 99%

A Decentralized Peer-to-Peer Energy Trading Model in Integrated Electric-Thermal System

Huang

Sun

et al. 2023

Preprint

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To promote the energy accommodation of both electrical and heating power while considering the source-load uncertainties, this paper proposes a peer-to-peer (P2P) energy trading model among prosumers in the integrated electric-thermal system, considering the energy trading agent (ETA) with self-build energy system. The Solar Heat-Pump Hybrid Thermal Water System (SPTS) and the transferring loss of heating power is considered and modelled based on the principle of steady-state thermal transfer. Since the variations of load and PV cannot be described by any single common distribution, the chance-constraints programming based on the Gaussian mixture model (GMM) is proposed to handle the uncertainty of the net load. As the proposed power trading problem is a non-convex problem with binary variables, an improved distributed alternating direction method of multiplier (ADMM) based on the predictor-corrector and two-stage cycle iterations is proposed to enhance the convergence performance of standard ADMM. Finally, an example simulation verifies the effectiveness, results show that the proposed model can decrease the total cost by 26.7% and enhance local energy balance by 61.8% compared to other cases.

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“…In the existing works, three commonly used approaches to tackle uncertainties are stochastic programming (SP) [1, 2], robust optimization (RO) [3, 4], and distributionally robust optimization (DRO) [5, 6]. Stochastic programming aims to minimize the expected costs given representative scenarios with known probability distribution functions (PDF), but the perfect information of PDF is hard to obtain in practical scheduling.…”

Section: Introductionmentioning

confidence: 99%

A multistage distributionally robust optimization approach for generation dispatch with demand response under endogenous and exogenous uncertainties

Huang,

Tai

et al. 2023

IET Generation Trans & Dist

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Decision‐dependent (endogenous) uncertainties (DDUs), as a new type of uncertainties revealed recently, couple dispatch decisions with uncertainty parameters and thus render power system dispatch more challenging. However, most previous works handled various DDUs via stochastic programming (SP) or robust optimization (RO) in a two‐stage framework, which undoubtedly introduces the drawbacks of SP and RO, and cannot meet the nonanticipativity requirements in power scheduling. In this paper, a multistage distributionally robust optimization (DRO) method for generation dispatch with demand response (DR) is proposed considering the DDUs of deferrable loads and the decision‐independent (exogenous) uncertainties (DIUs) of wind power and regular loads. By analyzing the structure of decision‐dependency parameters, a novel data‐driven decision‐dependent ambiguity set is proposed, which provides a generic framework for formulating DDUs and DIUs simultaneously. Then a multistage DRO model with nested max‐min structure is developed to integrate the merits of DRO and nonanticipativity into generation dispatch. The proposed model is solved by tailored reformulation method and improved stochastic dual dynamic integer programming (SDDiP). Case studies illustrate the effectiveness of the proposed approach by comparing with the multistage SP, RO, and decision‐independent DRO methods.

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Stochastic programming model for incentive‐based demand response considering complex uncertainties of consumers

Cited by 19 publications

References 35 publications

A collaborative training approach for multi energy systems in low‐carbon parks accounting for response characteristics

A collaborative training approach for multi energy systems in low‐carbon parks accounting for response characteristics

A Decentralized Peer-to-Peer Energy Trading Model in Integrated Electric-Thermal System

A multistage distributionally robust optimization approach for generation dispatch with demand response under endogenous and exogenous uncertainties

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