Optimal Price-Energy Demand Bids for Aggregate Price-Responsive Loads

Saez-Gallego, Javier; Kohansal, Mahdi; Sadeghi-Mobarakeh, Ashkan; Morales, Juan M.

doi:10.1109/tsg.2017.2677974

Cited by 30 publications

(36 citation statements)

References 22 publications

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“…Similar to [13,14], the spot EM bidding decision-making models for ER set up by robust optimization methods can also be reflected in [15][16][17][18][19]. As mentioned in [20], in spot EM circumstances and if neglecting intraday markets [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], an ER often submits price-energy demand bidding curve to the day-ahead market and only energy quantity bids to the balancing markets in order to counterbalance the deviations from the scheduled day-ahead loads to the corresponding actual ones. A price-energy demand bid is in fact a piecewise staircase price-energy curve [20,21], the number of which corresponds to the number of time units in a delivery day.…”

Section: Introductionmentioning

confidence: 98%

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

Wang

Sun

et al. 2019

Mathematical Problems in Engineering

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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.

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Section: Introductionmentioning

confidence: 98%

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

Wang

Sun

et al. 2019

Mathematical Problems in Engineering

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“…The day-ahead market quotation strategy model of selling by the electricity supplier was constructed. In [15,16], based on robust optimization and stochastic mixed integer programming, the optimal power purchase decision-making and quotation decision-making models for the day-ahead market of selling by the electricity supplier were proposed.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Optimal Scheduling Model of Microgrid Based on Chance-Constrained Programming in Spot Markets

Tan

Ren

et al. 2020

Processes

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Aimed at the coordination control problem of each unit caused by microgrid participation in the spot market and considering the randomness of wind and solar output and the uncertainty of spot market prices, a day-ahead real-time two-stage optimal scheduling model for microgrid was established by using the chance-constrained programming theory. On this basis, an improved particle swarm optimization (PSO) algorithm based on stochastic simulation technology was used to solve the problem and the effect of demand side management and confidence level on scheduling results is discussed. The example results verified the correctness and effectiveness of the proposed model, which can provide a theoretical basis in terms of reasonably coordinating the output of each unit in the microgrid in the spot market.

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“…A similar problem setting is presented in Sáez-Gallego et al [44] where a retailer buys energy in the day-ahead market for a pool of price-responsive consumers. They provide an analytic solution in the case that the retailer is not risk averse and a stochastic programming model for optimal bidding under risk aversion.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Demand Response in a Dynamic Capacity Expansion Model for the European Power Market

Marañón-Ledesma

Tomasgård

2019

Energies

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One of the challenges in the transition towards a zero-emission power system in Europe will be to achieve an efficient and reliable operation with a high share of intermittent generation. The objective of this paper is to analyse the role that Demand Response (DR) potentially can play in a cost-efficient development until 2050. The benefits of DR consist of integrating renewable source generation and reducing peak load consumption, leading to a reduction in generation, transmission, and storage capacity investments. The capabilities of DR are implemented in the European Model for Power Investments with high shares of Renewable Energy (EMPIRE), which is an electricity sector model for long-term capacity and transmission expansion. The model uses a multi-horizon stochastic approach including operational uncertainty with hourly resolution and multiple investment periods in the long-term. DR is modelled through several classes of shiftable and curtailable loads in residential, commercial, and industrial sectors, including flexibility periods, operational costs, losses, and endogenous DR investments, for 31 European countries. Results of the case study shows that DR capacity partially substitutes flexible supply-side capacity from peak gas plants and battery storage, through enabling more solar PV generation. A European DR capacity at 91 GW in 2050 reduces the peak plant capacities by 11% and storage capacity by 86%.

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Optimal Price-Energy Demand Bids for Aggregate Price-Responsive Loads

Cited by 30 publications

References 22 publications

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

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

A Two-Stage Optimal Scheduling Model of Microgrid Based on Chance-Constrained Programming in Spot Markets

Analyzing Demand Response in a Dynamic Capacity Expansion Model for the European Power Market

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