Energy and reserve dispatch with distributionally robust joint chance constraints

Ordoudis, Christos; Nguyên, Viêt Anh; Kühn, Daniel

doi:10.1016/j.orl.2021.01.012

Cited by 60 publications

(38 citation statements)

References 35 publications

(46 reference statements)

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“…The constraint of the system frequency in ( 17) is a joint chance constraint, and it can be split into two separated constraints according to Bernoulli's inequality [27], as…”

Section: P2: Minmentioning

confidence: 99%

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Automatic Generation Control Considering Uncertainties of the Key Parameters in the Frequency Response Model

Liu¹,

Hu²,

Mujeeb³

2021

Preprint

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The highly fluctuated renewable generations and electric vehicles have undergone tremendous growth in recent years. The majority of them are connected to the grid via power electronic devices, resulting in wide variation ranges for several key parameters in the frequency response model (FRM) such as system inertia and load damping factor. In this paper, an automatic generation control (AGC) method considering the uncertainties of these key parameters is proposed. First, the historical power system operation data following large power disturbances are used to identify the FRM key parameters offline. Second, the offline identification results and the normal operation data prior to the occurrence of the disturbance are used to train the online probability estimation model of the FRM key parameters. Third, the online estimation results of the FRM key parameters are used as the input, and the model predictive-based AGC signal optimization method is developed based on distributionally robust optimization (DRO) technology. Case studies conducted on the IEEE 118-Bus System show that the proposed AGC method outperforms the widely utilized PI-based control method in terms of performance and efficiency.<br>

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“…The constraint of the system frequency in ( 17) is a joint chance constraint, and it can be split into two separated constraints according to Bernoulli's inequality [27], as…”

Section: P2: Minmentioning

confidence: 99%

“…Constraints ( 31)-( 34) are transformed from chance constraint (27), where φ j,z , φ j,z , and ψ z are the dual variables of the constraints for ω j,z . ϑ j,z is an auxiliary variable.…”

Section: P2: Minmentioning

confidence: 99%

Automatic Generation Control Considering Uncertainties of the Key Parameters in the Frequency Response Model

Liu¹,

Hu²,

Mujeeb³

2021

Preprint

View full text Add to dashboard Cite

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Section: A Linear Decision Rule Approximatementioning

confidence: 99%

“…[19] [32]. Taking constraint (4) with probability1, for example, two deterministic constraints can be obtained by matching the zero-and first-order coefficients of on both sides of ( 13) [32]. Thus, the above semi-infinite problem with the decision rule approximation can be reexpressed as the following form,…”

Section: A Linear Decision Rule Approximatementioning

confidence: 99%

Multistage Model Predictive Control Co-Risk Dispatch for Coupled Electricity and Heat System

et al. 2021

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The energy transition can facilitate the rapid development of the coupled electricity and heat system (CEHS), accommodating the higher share of renewable energy. However, uncertain renewable energy sources (RESs) and overlimit risk can bring significant challenges for the safe operation of the CEHS. Considering uncertain RESs integration and overlimit risk within the upper and lower bounds jointly with high probability, the multistage model predictive control (MPC) co-risk dispatch model for the CEHS can be proposed in this paper. The model can hedge against the operational risk for the multistage rolling dispatch. In the context of the multistage real-time risk dispatch, the MPC framework is presented, which contains three processes, autoregressive moving average (ARMA) model prediction process, risk-based optimization process, and penalty-based feedback correction process. In the prediction process, the ARMA method can be employed to accurately predict the RESs power, which is characterized as the merit of the short-term forecast. In the optimization process, the violation risk of overlimit chance constraints caused by the output power fluctuation can be mitigated using the distributionally robust chance-constrained (DRCC) under the Wasserstein ambiguity set. The tractable approximation reformulation can be obtained by adopting the Worst-Case Conditional Value-at-Risk (WC-CVaR) approximation method. In the feedback correction process, penalty-based load shedding and RESs spillage can lower the risk level by minimizing the risk penalty cost. In the numerical case, the risk performance analysis based on the modified Barry Island system is implemented. The analysis demonstrates that the daily average violation probability can be mainly affected by the Wasserstein radius while the total cost can be affected by the violation probability . The dispatch energy for the CEHS is obtained using the proposed approach, which outperforms the existing robust optimization (RO) approach.

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“…To construct an ambiguity set, two paradigms exist in the literature, namely metric-and moment-based approaches. In the former, the ambiguity set encompasses all distributions that are close to an empirical distribution, measured by a probability distance metric [6]- [9]. In the latter, the ambiguity set contains all distributions satisfying identical moment constraints [2], [5].…”

Section: Introductionmentioning

confidence: 99%

Distributionally Robust Generation Expansion Planning With Unimodality and Risk Constraints

Pourahmadi

Kazempour

2021

IEEE Trans. Power Syst.

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As more renewables are integrated into the power system, capacity expansion planners need more advanced longterm decision-making tools to properly model short-term stochastic production uncertainty and to explore its effects on expansion decisions. We develop a distributionally robust generation expansion planning model, accounting for a family of potential probability distributions of wind forecast error uncertainty. Aiming to include more realistic distributions, we construct more informed moment-based ambiguity sets by adding structural information of unimodality. We include operational-stage unit commitment constraints and model the risk of operational limit violations in two distinct forms: chance and conditional value-at-risk (CVaR) constraints. In both forms, the resulting expansion planning model is a mixed-integer second-order cone program. Using a thorough out-of-sample numerical analysis, we conclude: (i) the distributionally robust chance-constrained generation expansion planning model exhibits a better out-ofsample performance only if sufficiently accurate information about the first-and the second-order moments as well as the mode location of potential distributions is available; (ii) conversely, if such accurate information is unavailable, the distributionally robust CVaR-constrained generation expansion planning model outperforms; (iii) these two models have a similar performance when unimodality information is excluded.

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Energy and reserve dispatch with distributionally robust joint chance constraints

Cited by 60 publications

References 35 publications

Automatic Generation Control Considering Uncertainties of the Key Parameters in the Frequency Response Model

Automatic Generation Control Considering Uncertainties of the Key Parameters in the Frequency Response Model

Multistage Model Predictive Control Co-Risk Dispatch for Coupled Electricity and Heat System

Distributionally Robust Generation Expansion Planning With Unimodality and Risk Constraints

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