CVaR-based stochastic wind-thermal generation coordination for Turkish electricity market

Aydoğdu, Aycan; Tör, Osman Bülent; Güven, Ali

doi:10.1007/s40565-018-0492-3

Cited by 11 publications

(7 citation statements)

References 20 publications

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“…Faced with uncertainties of regulation income caused by power spot price fluctuations and the prediction difficulty, CVAR is often used to assess market gains under certain risk appetite conditions [29, 30]. Taking a loss cost function

f (x, y)

as an example, the model principle of CVaR is given below.…”

Section: Aggregate Regulation Strategy Under the Power Spot Marketmentioning

confidence: 99%

“…On this basis, CVaR

ϕ_{α} false(x false)

considers the conditional mean value of VaR that the loss exceeds the confidence level, which is defined as:

\begin{matrix} ϕ_{α} (x) = E ([], f false(x, y false) | f false(x, y false) \geq φ_{α} (x)) = \\ \frac{1}{1 - α} \int_{f (x, y) \geq φ_{α} (x)} f (x, y) ρ (y) d y \end{matrix}

where

E [f false(x, y false) | f false(x, y false) \geq φ_{α} (x)]

represents the conditional mean function when the loss value exceeds the VaR value under the confidence level

α

. To facilitate the solution, the probability density function is often discretized [31]. The estimated value function

{\overset{ϕ}{true}}_{α} false(x, φ false)

of the simplified CVaR is:

{\overset{ϕ}{true}}_{α} false(x, φ false) = φ + \frac{1}{k (1 - α)} \sum_{k = 1}^{K} \max false[0, f (x, y_{k}) - φ false]

where

y_{k}

is the k ‐th group of sample data of

y

, a total of

K

groups.…”

Section: Aggregate Regulation Strategy Under the Power Spot Marketmentioning

confidence: 99%

“…f (x, y)𝜌(y)dy (12) where E[ f (x, y)| f (x, y) ≥ 𝜑 𝛼 (x)] represents the conditional mean function when the loss value exceeds the VaR value under the confidence level 𝛼. To facilitate the solution, the probability density function is often discretized [31]. The estimated value function φ𝛼 (x, 𝜑) of the simplified CVaR is:…”

Section: Income Risk Assessment Under the Fluctuation Of Power Spot P...mentioning

confidence: 99%

See 2 more Smart Citations

Aggregate regulation strategy of distributed energy storage under power spot market in China

Chen

et al. 2021

IET Renewable Power Gen

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The reform of power spot market in China provides a new profit mode, determining energy trading strategy based on the power spot prices for distributed energy storages. However, individually accessing every distributed energy storage to the dispatch centre results in a high cost and low efficiency, which needs to be improved by connecting through the aggregator. To this end, this paper proposes a regulation mode and strategy for distributed energy storages participating in energy trading through aggregation. First, the optimal centres of distributed energy storages are searched as the aggregation centres according to the electrical distance distributed by the energy storage, and the model of each distributed energy storage aggregation group is established. On this basis, the conditional value at risk (CVaR) method is introduced to quantify the income risk brought by the fluctuation of power spot prices, following with the optimization model for day‐ahead stage and real‐time stage. Finally, case studies under multiple scenarios of power spot market verify that the regulation mode and strategy can effectively guarantee the economic profits of distributed energy storages by setting aggregation groups and reasonable risk preference coefficients.

show abstract

f (x, y)

as an example, the model principle of CVaR is given below.…”

Section: Aggregate Regulation Strategy Under the Power Spot Marketmentioning

confidence: 99%

“…On this basis, CVaR

ϕ_{α} false(x false)

considers the conditional mean value of VaR that the loss exceeds the confidence level, which is defined as:

\begin{matrix} ϕ_{α} (x) = E ([], f false(x, y false) | f false(x, y false) \geq φ_{α} (x)) = \\ \frac{1}{1 - α} \int_{f (x, y) \geq φ_{α} (x)} f (x, y) ρ (y) d y \end{matrix}

where

E [f false(x, y false) | f false(x, y false) \geq φ_{α} (x)]

represents the conditional mean function when the loss value exceeds the VaR value under the confidence level

α

. To facilitate the solution, the probability density function is often discretized [31]. The estimated value function

{\overset{ϕ}{true}}_{α} false(x, φ false)

of the simplified CVaR is:

{\overset{ϕ}{true}}_{α} false(x, φ false) = φ + \frac{1}{k (1 - α)} \sum_{k = 1}^{K} \max false[0, f (x, y_{k}) - φ false]

where

y_{k}

is the k ‐th group of sample data of

y

, a total of

K

groups.…”

Section: Aggregate Regulation Strategy Under the Power Spot Marketmentioning

confidence: 99%

Section: Income Risk Assessment Under the Fluctuation Of Power Spot P...mentioning

confidence: 99%

See 1 more Smart Citation

Aggregate regulation strategy of distributed energy storage under power spot market in China

Chen

et al. 2021

IET Renewable Power Gen

View full text Add to dashboard Cite

show abstract

“…Literature [1] establishes a two-layer multi-agent decision optimization model including renewable energy day-ahead market and residual renewable energy consumption. Literature [2] adopts CVaR to describe the uncertainty of wind power output and proposes a two-stage optimization model for coordinated participation of wind and thermal power in the day-ahead-real-time market.Literature [3] optimizes the joint wind-photovoltaic-storage participation in the power market strategy and shows that the benefits of joint wind-photovoltaic-storage participation in the power market are higher than the benefits of independent operation of wind and PV units. Literature [4] adopts a stochastic programming approach to deal with the uncertainty of wind farm output power, dayahead and real-time market prices, and proposes a trading strategy for offshore wind power entities to participate in the spot market with optimal returns.…”

Section: Introductionmentioning

confidence: 99%

A Medium and Long Term Contract Trading Model for New Energy Units Based on Incomplete Information Game Theory

Dong,

Gao,

et al. 2024

Proceedings of the 5th International Conference on Economic Management and Model Engineering, ICEMME 2023, November 17–19, 2023

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Under the goal of carbon neutrality and carbon peaking, a high percentage of wind power and photovoltaics access to the power system. As China's power trading mechanism improves, new energy is encouraged to be consumed through participation in power trading. Under the centralized power market, the medium and long-term trading power accounts for more than 90% of all traded power. This paper is based on the characteristics of the contract trading in medium-term and long-term, and volatility of new energy output, to construct incomplete information game trading model of the new energy units and power users. At the same time, according to the power medium and long-term trading to form the requirements of the trading curve, selected a trading day in the monthly trading as the object, the trading day 24 hours of trading electricity and price of the game simulation analysis. The results show that the game method effectively improves the overall income of new energy units participating in contract trading, and the price formed by the game can better respond to the supply and demand relationship in the market.

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“…23 Currently, CVaR is employed widely in problems related to electricity markets due to its linear formulation and also being a coherent risk measure. 29…”

Section: Introductionmentioning

confidence: 99%

Risk‐averse integrated demand response and dynamic G2V charge scheduling of an electric vehicle aggregator to support grid stability

Sharma

Jain

2021

Int Trans Electr Energ Syst

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Electric vehicle aggregator (EVA) trades in energy and ancillary service markets for profit maximisation through grid-to-vehicle (G2V) charge scheduling of EVs. It can provide regulation services to system operator (SO) through coordinated and distributed charging of EVs. However, EV owner aim at minimum charging cost. Price-based demand response (PBDR) integrated charge scheduling can motivate EVs by off peak charging for reduced cost along with system load levelling. Real-time (RT) PBDR, adopted in literature, has low acceptance rate by EVs for being too dynamic to respond and being infeasible for few charge cycles in a day. Time of use (TOU) PBDR, being less dynamic, is a natural price signal for EVs and shields them from RTP volatility as well.Additionally, EV charging cost constrained EVA's charge scheduling is a realistic formulation and is subjected to multiple uncertainties like EVs' mobility behaviour and market prices. Considering this, risk-averse charge scheduling of EVA with TOU PBDR and EV charging cost constraint is proposed. EVs' mobility behaviour and market prices' uncertainties are modeled through stochastic programming. Agglomerative hierarchical clustering is proposed to develop TOU price for EVA from RTP. Conditional value at risk (CVaR) is used as risk measure. Results of a case study of EVA with 1000 EVs illustrate a List of symbols and abbreviations: N s , set of the scenarios with index s; T, set of the timeslots with index t (h); N, set of the electric vehicles with index i; P max i =P min i , upper/lower charging rate limit (kW) for EV i; BC i , the battery capacity of i th EV (kWh); Ef i , charging efficiency of EV i; TDC, the deliverable capacity of distribution transformer (MW); M t , markup pricing

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CVaR-based stochastic wind-thermal generation coordination for Turkish electricity market

Cited by 11 publications

References 20 publications

Aggregate regulation strategy of distributed energy storage under power spot market in China

Aggregate regulation strategy of distributed energy storage under power spot market in China

A Medium and Long Term Contract Trading Model for New Energy Units Based on Incomplete Information Game Theory

Risk‐averse integrated demand response and dynamic G2V charge scheduling of an electric vehicle aggregator to support grid stability

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