Stochastic Bidding Research Articles

Applying and benchmarking a stochastic programming-based bidding strategy for day-ahead hydropower scheduling

Aneo is one of the first Nordic power companies to apply stochastic programming for day-ahead bidding of hydropower. This paper describes our experiences in implementing, testing, and operating a stochastic programming-based bidding method aimed at setting up an automated process for day-ahead bidding. The implementation process has faced challenges such as generating price scenarios for the optimization model, post-processing optimization results to create feasible and understandable bids, and technically integrating these into operational systems. Additionally, comparing the bids from the new stochastic-based method to the existing operator-determined bids has been challenging, which is crucial for building trust in new procedures. Our solution is a rolling horizon comparison, benchmarking the performance of the bidding methods over consecutive two-week periods. Our benchmarking results show that the stochastic method can replicate the current operator-determined bidding strategy. However, additional work is needed before we can fully automate the stochastic bidding setup, particularly in addressing inflow uncertainty and managing special constraints on our watercourses.

Stochastic bidding for VPPs enabled ancillary services: A case study

Stochastic bidding for VPPs enabled ancillary services: A case study

A two-stage optimization model for road-rail transshipment procurement and truckload synergetic routing

A two-stage optimization model for road-rail transshipment procurement and truckload synergetic routing

Stochastic programming approach for optimal day-ahead market bidding curves of a microgrid

The deregulation of electricity markets has driven the need to optimise market bidding strategies, e.g. when and how much electricity to buy or sell, in order to gain an economic advantage in a competitive market environment. The present work aims to determine optimal day-ahead market bidding curves for a microgrid comprised of a battery, power generator, photovoltaic (PV) system and an electricity load from a commercial building. Existing day-ahead market bidding models heuristically fix price values for each allowed bidding curve point prior to the optimisation problem or relax limitations set by market rules on the number of price–quantity points per curve. In contrast, this work integrates the optimal selection of prices for the construction of day-ahead market bidding curves into the optimisation of the energy system schedule; aiming to further enhance the bidding curve accuracy while remaining feasible under present market rules. The examined optimisation problem is formulated as a mixed integer linear programming (MILP) model, embedded in a two-stage stochastic programming approach. Uncertainty is considered in the electricity price and the PV power. First stage decisions are day-ahead market bidding curves, while the overall objective is to minimise the expected operational cost of the microgrid. The bidding strategy derived is then examined through Monte Carlo simulations by comparing it against a deterministic approach and two alternative stochastic bidding approaches from literature.

A decentralized stochastic bidding strategy for aggregators of prosumers in electricity reserve markets

A decentralized stochastic bidding strategy for aggregators of prosumers in electricity reserve markets

Bidding and Charging Scheduling Optimization for the Urban Electric Bus Operator

With the development of electric buses (EBs) in urban areas, how to effectively utilize their charging demand flexibility and the considerable energy stored in the on-board batteries becomes an urgent issue for the electric bus operator (EBO) to consider. This paper focuses on the bidding decision in energy and reserve markets for the urban EBO owning multiple electric bus stations with energy storage systems (ESSs), and the charging scheduling of all EBs. A trip-chain based electric vehicle boundary model is proposed firstly to concisely describe the flexibility region for an EB with multiple sequential trips, which can be further aggregated via a state-based approach to adapt to large-scale problems. The uncertain boundaries are also derived taking the uncertainty of trip energy consumption into account. In order to deal with the issue that the boundary aggregation will possibly influence the feasibility of decomposing the total power to each individual EB, the stochastic bidding and EB scheduling optimization problem in a hierarchical optimization framework is proposed. Accelerated Benders decomposition is utilized to efficiently solve the problem. Case studies verify that the proposed model can dramatically reduce the computational burden, effectively consider the uncertainty and take advantage of the flexibility of both EBs and ESSs to achieve better economic performance.

Stochastic local flexibility market design, bidding, and dispatch for distribution grid operations

In order to unlock the flexibility potential of energy consumers and prosumers, the development of market mechanisms for flexibility planning and procurement is necessary. The authors propose a stochastic local flexibility market to solve grid issues such as voltage deviations and grid congestion in a distribution grid. Their proposed solution includes activation of flexibility assets at the consumers’ premises, using a stochastic local flexibility market design. They consider a pooled local flexibility market design under demand uncertainty and stochastic bidding process. Optimization models are used to determine flexibility demand and supply bids by the distribution system operator and the aggregator respectively. A stochastic AC-optimal power model to determine flexibility demand and a two-stage stochastic model to supply flexibility are implemented to simulate a stochastic local flexibility market. This allows to determine stochastic flexibility supply bid curves, and optimum flexibility supply dispatch. The analysis shows that the cost of grid operations is reduced when the system uses the local flexibility market. The proposed methodology is applicable for local flexibility market designs aiming to use potential end-user flexibility for grid operations.

Stochastic Optimal Bidding Strategy for Energy and Ancillary Services in Microgrid

With the transformation of electricity markets from vertical integration to horizontally distributed systems and because of the significant penetration of renewables into the grid, participation of renewables in the electricity trading process has become mandatory. Due to the uncertainties in the energy dispatched by renewables, central aggregators have been penalizing the nonconventional energy for the imbalances in the submitted bids. These penalties discourage the renewables from engaging as market participants. This article has proposed a two-stage stochastic joint bidding strategy for wind power plant (WPP) and pumped storage plant (PSP) in the microgrid to overcome these limitations. In the first stage, collective energy and ancillary service bids are optimized based on the day ahead energy and ancillary service prices. In the second stage, different wind power scenarios are realized based on historical patterns, and PSP reduces the deviations in the submitted joint bids in the energy and ancillary service markets. The proposed stochastic bidding strategy is demonstrated by a WPP of capacity 3 MW, and six PSP units of each capacity 1 MW. This stochastic bidding strategy hedges the risk introduced by renewables. Moreover, this bidding strategy compares the two ancillary services, spinning reserve (SR) and fast response reserve (FRR), regarding expected revenue and penalty for the imbalances in the submitted joint energy bids.

Day-ahead optimal bidding of microgrids considering uncertainties of price and renewable energy resources

Day-ahead optimal bidding of microgrids considering uncertainties of price and renewable energy resources

A Decentralized Electricity Trading Framework (DETF) for Connected EVs: A Blockchain and Machine Learning for Profit Margin Optimization

Connected electric vehicles (CEVs) can help cities to reduce road congestion and increase road safety. With the technical improvement made to the battery system in terms of capacity and flexibility, CEVs, as mobile power plants can be an important actor for the electricity markets. Especially, they can trade electricity between each other when supply stations are full or temporarily not available. In this article, we propose an advanced decentralized electricity trading framework between CEVs in parking lots based on consortium blockchain, machine learning, and Game theoretic model. We design a distributed smart contract solution based on a stochastic bidding process, which helps CEVs to sell and buy electricity with their maximum profitability. Finally, numerical simulations with MATLAB and Solidity are conducted to prove the effectiveness of our proposed solution. Also, a comparison with another method in terms of CEVs' profitability improvement and energy trading management is provided.

Impact of electricity price forecasting errors on bidding: a price‐taker's perspective

Electricity price forecasting is very important for market participants in a deregulated market. However, only a few papers investigated the impact of forecasting errors on the market participants' behaviours and revenues. In this study, a general formulation of bidding in the electricity market is considered and the participant is assumed to be a price-taker which is general for most of the participants in power markets. A numerical method for quantifying the impact of forecasting errors on the bidding curves and revenues based on multiparametric linear programming is proposed. The forecasted prices are regarded as exogenous parameters for both deterministic and stochastic bidding models. Compared with the existing method, the proposed method can calculate how much improvement will be achieved in the cost or revenue of the bidder if he reduces the price forecasting error level, and such calculation does not require any predefined forecasting results. Numerical results and discussions based on real-market price data are conducted to show the application of the proposed method.

Stochastic bidding strategy of electric vehicles and energy storage systems in uncertain reserve market

This paper proposes an Electric Vehicle (EV) aggregator bidding strategy in the reserve market. The EV aggregator determines the charging/discharging operations of EVs in providing reserve service for profits maximization. In the Day-Ahead Market (DAM), the EV aggregator submits a bidding plan to the Independent Systems Operator (ISO) including base-load and reserve up/down capacities plans. In the Real-Time Market (RTM), the EV aggregator should deploy reserve based on the ISO's requirements, and the EV aggregator could receive income by deploying reserve or penalty for reserve shortage. The stochastic programming method is applied to address the uncertain reserve deployment requirements in RTM. In addition, Energy Storage Systems (ESS) are utilized by the EV aggregator to enhance the ability in providing reserve service. The aggregator–owner contract is designed to guarantee EV owners' economic benefits. Case studies show the expected profits of the EV aggregator are maximized and the risk of the reserve shortage is well managed, i.e., penalty is minimized. With the utilization of ESS, the performance of the EV aggregator in making response to the ISO's requirements is improved. That is, the required reserve percentage increases from 5.68% to 7.85%, and the deployed reserve percentage increases from 69.71% to 88.47%.

Stochastic bidding of volume and price in constrained energy and reserve markets

The power system is undergoing a significant change as it adapts to the intermittency and uncertainty from renewable generation. Flexibility from loads such as electric vehicles (EVs) can serve as reserves to sustain the supply-demand balance in the grid. Some reserve markets have rules for participation that are computationally challenging for aggregators of such flexible loads: they are asked to bid both volume and price, and on top of this there is a minimum-volume requirement, a constraint currently under discussion both in the US and European markets. Several state-of-the-art methods to find a bidding strategy for the demand scheduling of large fleets of flexible loads in the day-ahead and reserve market are adapted to deal with such a shared constraint, and are compared based on costs, unscheduled demand, and running time. The experimental analysis shows that although such a shared constraint significantly affects scalability, some of the proposed adaptations can deal with this without much loss in quality. This comparison also shows the importance of including good uncertainty models for dealing with the risk of not meeting the users’ demands, and that it is possible to find an optimal single price per time unit for scheduling a fleet of EVs.

Stochastic bidding strategy for electrical vehicle charging stations to participate in frequency containment reserves markets

This study presents a stochastic bidding strategy for electrical vehicle charging stations (EVCSs) to participate in frequency containment reserves (FCRs) markets. To achieve this, the study starts by developing deterministic models to calculate the maximum FCR that could be provided by each charging event (cycle) of an electric vehicle. These models are established based on the technical requirements of FCR in the Nordic flexibility market, namely the frequency containment reserve for normal operation and frequency containment reserve for disturbances. These deterministic models will be combined with historical data of charging records in EVCS to develop a methodology to calculate the probability density functions of the FCR profiles. Finally, the optimum FCR profiles, which maximise the expected profit of EVCS from participating in the day-ahead flexibility market, are estimated by performing a stochastic optimisation. The proposed methodology is evaluated by using empirical charging data of public EVCS in the Helsinki area.

Location-based distribution estimation for stochastic bid price optimization

ABSTRACT Stochastic bid price optimization of truckload carriers in simultaneous independent transportation auctions requires estimating the probability distributions of the clearing prices. Historical data can be used for this purpose. The sole estimation method found in the literature for this problem setting only takes the length of the auction load into account. In this paper, we devise methods for load-specific parameter estimation by filtering out data coming from past auction loads with distant origin or destination locations. Through simulations, we demonstrate that our methods can improve the profitability of the carriers compared to the previously used method.

Worst-case conditional value-at-risk based bidding strategy for wind-hydro hybrid systems under probability distribution uncertainties

Worst-case conditional value-at-risk based bidding strategy for wind-hydro hybrid systems under probability distribution uncertainties

Optimal Market Participation of Distributed Load Resources Under Distribution Network Operational Limits and Renewable Generation Uncertainties

Distributed load resources are encouraged nowadays to actively participate in the energy market. As a part of the distribution system, they affect the power flow pattern of the network and interact with intermittent renewable generation in the distribution system. In this regard, one fundamental challenge, not yet addressed, is to derive an optimal market participation model, under the demand bidding paradigm, that systemically accounts for the operational limits of a physical distribution grid considering uncertainty associated with both the electricity market , and distribution network system . Accordingly, this paper addresses the optimal demand biding under uncertain market and distribution system data and network operational limits. Assuming a price-taker distribution utility with renewable energy, inflexible and deferrable loads and a two-settlement market model, we develop a two-stage robust stochastic bidding formulation solved using a decomposition algorithm. We derive optimal bid curves that minimize energy procurement cost and fully comply with the operational standards of the distribution network. Moreover, novel indexes are proposed to help the utility evaluate the operational performance of its network with regard to deferrable loads and renewable resources. Finally, we illustrate the advantage of the proposed model from a set of numerical experiments on an example system and the 33-bus system.

Self-Scheduling Approach to Coordinating Wind Power Producers With Energy Storage and Demand Response

The uncertainty of wind energy makes wind power producers (WPPs) incur profit/loss due to balancing costs in electricity markets, a phenomenon that restricts their participation in markets. This paper proposes a stochastic bidding strategy based on virtual power plants (VPPs) to increase the profit of WPPs in short-term electricity markets in coordination with energy storage systems and demand response. To implement the stochastic solution strategy, the Kantorovich method is used for scenario generation and reduction. The optimization problem is formulated as a Mixed-Integer Linear Programming problem. From testing the proposed method for a Spanish WPP, it is inferred that the proposed method enhances the profit of the VPP compared to previous models.

Trading strategy optimization for a prosumer in continuous double auction-based peer-to-peer market: A prediction-integration model

Trading strategy optimization for a prosumer in continuous double auction-based peer-to-peer market: A prediction-integration model

Risk-aware stochastic bidding strategy of renewable micro-grids in day-ahead and real-time markets

Risk-aware stochastic bidding strategy of renewable micro-grids in day-ahead and real-time markets