scholarly journals Data-driven risk-based scheduling of energy communities participating in day-ahead and real-time electricity markets

2021 ◽  
Author(s):  
Mihály Dolányi ◽  
Kenneth Bruninx ◽  
Jean-François Toubeau ◽  
Erik Delarue
Keyword(s):  
Real Time ◽  
Electricity Markets ◽  
Value At Risk ◽  
Large Scale ◽  
Real Data ◽  
Conditional Value At Risk ◽  
Limited Information ◽  
Worst Case ◽  
Flexible Resources ◽  
Energy Community

<div>This paper presents new risk-based constraints for the participation of an energy community in day-ahead and real-time energy markets. Forming communities offers indeed an effective way to manage the risk of the overall portfolio by pooling individual resources and associated uncertainties. However, the diversity of flexible resources and the related user-specific comfort constraints make it difficult to properly represent flexibility requirements and to monetize constraint violations.</div><div>To address these issues, we propose a new risk-aware probabilistic enforcement of flexibility constraints using the conditional-value-at-risk (CVaR). Next, an extended version of the model is introduced to mitigate the distributional ambiguity faced by the community manager when new sites with limited information are embedded in the portfolio. This is achieved by defining the worst-case CVaR based-constraint (WCVaR-BC) that differentiates the CVaR value among different sub-clusters of clients.</div><div>Both reformulations are linear, thus allowing to tackle large-scale stochastic problems. The proposed risk-based constraints are then trained and evaluated on real data collected from several industrial sites. Our findings indicate that using the WCVaR-BC leads to systematically higher out-of-sample reliability, while decreasing the exposure to extreme outcomes.</div>

scholarly journals Data-driven risk-based scheduling of energy communities participating in day-ahead and real-time electricity markets

2021 ◽  
Author(s):  
Mihály Dolányi ◽  
Kenneth Bruninx ◽  
Jean-François Toubeau ◽  
Erik Delarue
Keyword(s):  
Real Time ◽  
Electricity Markets ◽  
Value At Risk ◽  
Large Scale ◽  
Real Data ◽  
Conditional Value At Risk ◽  
Limited Information ◽  
Worst Case ◽  
Flexible Resources ◽  
Energy Community

<div>This paper presents new risk-based constraints for the participation of an energy community in day-ahead and real-time energy markets. Forming communities offers indeed an effective way to manage the risk of the overall portfolio by pooling individual resources and associated uncertainties. However, the diversity of flexible resources and the related user-specific comfort constraints make it difficult to properly represent flexibility requirements and to monetize constraint violations.</div><div>To address these issues, we propose a new risk-aware probabilistic enforcement of flexibility constraints using the conditional-value-at-risk (CVaR). Next, an extended version of the model is introduced to mitigate the distributional ambiguity faced by the community manager when new sites with limited information are embedded in the portfolio. This is achieved by defining the worst-case CVaR based-constraint (WCVaR-BC) that differentiates the CVaR value among different sub-clusters of clients.</div><div>Both reformulations are linear, thus allowing to tackle large-scale stochastic problems. The proposed risk-based constraints are then trained and evaluated on real data collected from several industrial sites. Our findings indicate that using the WCVaR-BC leads to systematically higher out-of-sample reliability, while decreasing the exposure to extreme outcomes.</div>

scholarly journals Risk-Limiting Real-Time Economic Dispatch in a Power System with Flexibility Resources

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3133 ◽  
Author(s):  
Hongji Lin ◽  
Chongyu Wang ◽  
Fushuan Wen ◽  
Chung-Li Tseng ◽  
Jiahua Hu ◽  
...  
Keyword(s):  
Power System ◽  
Real Time ◽  
Value At Risk ◽  
Renewable Energy Sources ◽  
Economic Dispatch ◽  
Conditional Value At Risk ◽  
Operational Flexibility ◽  
Practical Applications ◽  
Start Up ◽  
The Impact

The integration of numerous intermittent renewable energy sources (IRESs) poses challenges to the power supply-demand balance due to the inherent intermittent and uncertain power outputs of IRESs, which requires higher operational flexibility of the power system. The deployment of flexible ramping products (FRPs) provides a new alternative to accommodate the high penetration of IRESs. Given this background, a bi-level risk-limiting real-time unit commitment/real-time economic dispatch model considering FRPs provided by different flexibility resources is proposed. In the proposed model, the objective is to maximize the social surplus while minimizing the operational risk, quantified using the concept of conditional value-at-risk (CVaR). Energy and ramping capabilities of conventional generating units during the start-up or shut-down processes are considered, while meeting the constraints including unit start-up/shut-down trajectories and ramping up/down rates in consecutive time periods. The Karush–Kuhn–Tucker (KKT) optimality conditions are then used to convert the bi-level programming problem into a single-level one, which can be directly solved after linearization. The modified IEEE 14-bus power system is employed to demonstrate the proposed method, and the role of FRPs in enhancing the system flexibility and improving the accommodation capability for IRESs is illustrated in some operation scenarios of the sample system. The impact of the confidence level in CVaR on the system operational flexibility is also investigated through case studies. Finally, a case study is conducted on a regional power system in Guangdong Province, China to demonstrate the potential of the proposed method for practical applications.

scholarly journals Risk-based constraints with correlated uncertainties for the optimal operation of an energy community

2021 ◽  
Author(s):  
Mihály Dolányi ◽  
Kenneth Bruninx ◽  
Jean-François Toubeau ◽  
Erik Delarue
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Measurement Data ◽  
Real Life ◽  
Optimal Operation ◽  
Conditional Value At Risk ◽  
Forecast Errors ◽  
Worst Case ◽  
Life Measurement ◽  
Out Of Sample

<div>This paper formulates an energy community's centralized optimal bidding and scheduling problem as a time-series scenario-driven stochastic optimization model, building on real-life measurement data. In the presented model, a surrogate battery storage system with uncertain state-of-charge (SoC) bounds approximates the portfolio's aggregated flexibility. </div><div>First, it is emphasized in a stylized analysis that risk-based energy constraints are highly beneficial (compared to chance-constraints) in coordinating distributed assets with unknown costs of constraint violation, as they limit both violation magnitude and probability. The presented research extends state-of-the-art models by implementing a worst-case conditional value at risk (WCVaR) based constraint for the storage SoC bounds. Then, an extensive numerical comparison is conducted to analyze the trade-off between out-of-sample violations and expected objective values, revealing that the proposed WCVaR based constraint shields significantly better against extreme out-of-sample outcomes than the conditional value at risk based equivalent.</div><div>To bypass the non-trivial task of capturing the underlying time and asset-dependent uncertain processes, real-life measurement data is directly leveraged for both imbalance market uncertainty and load forecast errors. For this purpose, a shape-based clustering method is implemented to capture the input scenarios' temporal characteristics.</div>

Analysis of freight train collision risk in the United States

2018 ◽  
Vol 233 (8) ◽  
pp. 817-830 ◽  
Author(s):  
Tejashree Turla ◽  
Xiang Liu ◽  
Zhipeng Zhang
Keyword(s):  
United States ◽  
Value At Risk ◽  
The United States ◽  
Mitigation Strategies ◽  
Conditional Value At Risk ◽  
Collision Risk ◽  
Worst Case ◽  
Freight Train ◽  
The Us ◽  
Traffic Exposure

Rail transportation is pivotal for the national economy. Despite being rare, a train accident can potentially result in severe consequences, such as infrastructure damage costs, casualties, and environmental impacts. An understanding of accident frequency, severity, and risk is important for rail safety management. In the United States, extensive prior research has focused on risk analyses of train derailments and highway–rail grade crossing accidents. Relatively less work has been conducted regarding train collision risk. The US Federal Railroad Administration identifies various accident causes, among which the authors of this study have analyzed the major collision causes. For each major accident cause, the authors have analyzed its resultant collision frequency, severity (in terms of damage cost or casualties), and correspondingly the risk, which is the combination of the frequency and severity. The analysis was based on train collision data in the United States from 2001 to 2015. This analysis focuses on freight trains in the United States, due to their immense traffic exposure. On the temporal scale, collision rate (the number of collisions normalized by traffic exposure) has an approximately 5% annual reduction. In terms of collision cause, failures to obey signals, overspeeds, and violations of mainline operating rules accounted for more collisions than other causes. Two alternative risk measures, namely the expected consequence and conditional value at risk, were used to evaluate the freight train collision risk on main tracks, accounting for both the average and worst-case scenarios. This collision risk analysis methodology may provide the US Department of Transportation and railroad industry with information and decision support for identifying, evaluating, and implementing cost-effective risk mitigation strategies.

USING EXTREME VALUE THEORY TO EVALUATE CONDITIONAL VAR FOR RISK MANAGEMENT IN ELECTRICITY MARKETS

2016 ◽  
Vol 78 (10) ◽  
Author(s):  
M. T. Askari ◽  
Z. Afzalipor ◽  
A. Amoozadeh
Keyword(s):  
At Risk ◽  
Electricity Markets ◽  
Extreme Value Theory ◽  
Value At Risk ◽  
Risk Model ◽  
Test System ◽  
Value Theory ◽  
Extreme Value ◽  
Conditional Value At Risk ◽  
Bidding Strategy

In a deregulated power market, generation companies attempt to maximize their profits and minimize their risks. This paper proposes a risk model for bidding strategy of generation companies based on EVT-CVaR method. Extreme Value Theory can overcome shortcomings of traditional methods in computing financial risk based on value-at-risk and conditional value-at-risk method. Also, generalized Pareto distribution is suggested to model tail of an unknown distribution and parameters of the GPD are estimated by likelihood moment method. Numerical results for risk assessment using the proposed approach are presented for IEEE 30-bus test system. According to the findings, this method can be used as a robust technique to calculate the risk for bidding strategy of generation companies.

scholarly journals Optimal Day-Ahead Bidding Strategy for Electricity Retailer with Inner-Outer 2-Layer Model System Based on Stochastic Mixed-Integer Optimization

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yuwei Wang ◽  
Jingmin Wang ◽  
Wei Sun ◽  
Mingrui Zhao
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Value At Risk ◽  
Model System ◽  
Mixed Integer ◽  
Conditional Value At Risk ◽  
Integer Optimization ◽  
Layer Model ◽  
Mixed Integer Optimization ◽  
Near Future

Bidding in spot electricity market (EM) is a key source for electricity retailer (ER)’s power purchasing. In China for the near future, besides the real-time load and spot clearing prices uncertainties, it will be hard for a newborn ER to adjust its retail prices at will due to the strict governmental supervision. Hence, spot EM bidding decision-making is a very complicated and important issue for ER in many countries including China. In this paper, an inner-outer 2-layer model system based on stochastic mixed-integer optimization is proposed for ER’s day-ahead EM bidding decision-making. This model system not only can help to make ERs more beneficial under China’s EM circumstances in the near future, but also can be applied for improving their profits under many other deregulated EM circumstances (e.g., PJM and Nord Pool) if slight transformation is implemented. Different from many existing researches, we pursue optimizing both the number of blocks in ER’s day-ahead piecewise staircase (energy-price) bidding curves and the bidding price of every block. Specifically, the inner layer of this system is in fact a stochastic mixed-integer optimization model, by which the bidding prices are optimized by parameterizing the number of blocks in bidding curves. The outer layer of this system implicitly possesses the characteristics of heuristic optimization in discrete space, by which the number of blocks is optimized by parameterizing bidding prices in bidding curves. Moreover, in order to maintain relatively low financial-risk brought by clearing prices and real-time load uncertainties, we introduce the conditional value at risk (CVaR) of profit in the objective function of inner layer model in addition to the expected profit. Simulations based on historical data have not only tested the scientificity and feasibility of our model system, but also verified that our model system can further improve the actual profit of ER compared to other methods.

scholarly journals Multi-Level Market Transaction Optimization Model for Electricity Sales Companies with Energy Storage Plant

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 145 ◽  
Author(s):  
Guan Wang ◽  
Zhongfu Tan ◽  
Hongyu Lin ◽  
Qingkun Tan ◽  
Shenbo Yang ◽  
...  
Keyword(s):  
Real Time ◽  
Value At Risk ◽  
Market Price ◽  
Market Risk ◽  
Mixed Integer ◽  
Conditional Value At Risk ◽  
The Real ◽  
Market Transaction ◽  
Electricity Load ◽  
Multi Level

Due to market price uncertainty and volatility, electricity sales companies today are facing greater risks in regard to the day-ahead market and the real-time market. Along with introducing the Time of Use (TOU) price for the customer as a type of balancing resource to avoid market risk, electricity sales companies should adopt the market risk-aversion method to reduce the high cost of ancillary services in the real-time market by using multi-level market transactions, as well as to provide a reference for the profits of power companies. In this paper, we establish a non-linear mathematical model based on stochastic programming by using conditional value-at-risk (CVaR) to measure transaction strategy risk. For the market price and consumer electricity load as the uncertain factors of multi-level market transactions of electricity sales companies, the optimal objective was to maximize the revenue of electricity sales companies and minimize the peak-valley differences in the system, which is solved by using mixed-integer linear programming (MILP). Finally, we provide an example to analyze the effect of the fluctuation degree of customer load and market price on the profit of electricity sales companies under different confidence coefficients.

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