Bidding Strategies Research Articles

Milkbasket takeover by Reliance Retail Venture Limited: the ethical dilemma

Learning outcomes After completion of the case study, the students would be able to comprehend the crisis at hand for Milkbasket, why did it resist Reliance’s takeover in the first place, then to evaluate the pros-cons and future prospects for the organization post-acquisition; to evaluate from an ethical standpoint the process of mergers and acquisitions using ethical frameworks to understand how, when, to whom and through what processes do mergers and acquisitions qualify the test of being ethical; and to analyse different hostile takeovers, especially through tender offers, proxy contests and toehold bidding strategy in this case. Case overview/synopsis Founded in 2015, Milkbasket was a micro-delivery start-up based in Gurugram (near New Delhi), India. Milkbasket would let its subscribers order till midnight and deliver groceries, milk and other everyday essentials to its subscribers before 7 a.m. next day. It had burnt a lot of cash and was facing difficulty in getting investors; as such they were engaged in discussions with many companies. Two of them – Reliance Retail Venture Limited and BigBasket – were not accepting the proposed valuation, but Milkbasket got term sheets from other two companies. Complexity academic level This case study can be used for graduate courses on strategic management, business ethics and corporate governance. This case study can also be used in corporate finance course to highlight the importance of making ethical/responsible judgements to protect stakeholder interests. Supplementary materials Teaching notes are available for educators only. Subject code CSS 3: Entrepreneurship.

TD-MDB: A Truth-Discovery-Based Multidimensional Bidding Strategy for Federated Learning in Industrial IoT Systems

TD-MDB: A Truth-Discovery-Based Multidimensional Bidding Strategy for Federated Learning in Industrial IoT Systems

Optimal bidding strategy of a wind power producer in Chinese spot market considering green certificate trading.

In China, wind power producers will participate in the spot market as strategic producers. They should submit offering prices and forecasted production to the independent system operator. Intraprovincial and interprovincial green certificate trading, as a mechanism to promote the development of wind power, is advanced in parallel with the spot market. Studying the bidding strategies of wind power producers in the spot market, especially with the introduction of intraprovincial and interprovincial green certificate trading, has great practical significance for the stable operation of wind power producers and the construction of a renewable energy-friendly electricity market. This paper proposes a bi-level stochastic model to simulate the bidding strategy of a wind power producer in the spot market. The upper level problem of the model describes the decision-making process of the wind power producer, and the lower level problems include the clearing process of the spot market and the TGC market, in which the spot market includes the day-ahead market and the balance market, and the TGC market includes the intraprovincial and interprovincial TGC markets. Then, using Karush-Kuhn-Tucker (KKT) conditions, the strong duality theorem, and the big M method, the bi-level model is recast into a mixed integer linear (MIL) model convenient to be solved. Finally, an example is given to analyze the wind power producer's optimal bidding strategy under different electricity market environments and different wind power penetration levels.

Capacity Allocation Method of Pumped-Storage Power Station for Multi-Level Market in New Power System

With the development of the electricity spot market, pumped-storage power stations are faced with the problem of realizing flexible adjustment capabilities and limited profit margins under the current two-part electricity price system. At the same time, the penetration rate of new energy has increased. Its uncertainty has brought great pressure to the operation of the power system. The ramp market and its market mechanism have emerged as the times require. To this end, this article proposes a bidding strategy for pumped-storage power stations to participate in multi-level markets such as the ramp market. Considering the demand calculation of ramping services, a two-layer model of pumped storage’s participation in multiple markets is constructed. The upper level makes trading decisions with the goal of maximizing pumped-storage revenue; the lower level aims to minimize the total social cost and jointly clears the primary and auxiliary markets. The income from pumped storage participating in the main energy and ramp-up auxiliary markets at the same time is significantly higher than the income from the two-part electricity price system. Its flexible adjustment ability can be quantified, reducing dependence on capacity electricity charges and providing a theoretical reference for cost recovery and profitability of future pumped-storage power stations.

Bidding strategy and economic evaluation of energy storage systems under the time-of-use pricing mechanism

Energy storage systems (ESSs) can smooth loads, effectively enable demand-side management, and promote renewable energy consumption. This study developed a two-stage bidding strategy and economic evaluation model for ESS. In the first stage, time-of-use (TOU) pricing model based on the consumer psychology theory and user demand response function is proposed. In the second stage, the benefits and life cycle costs of ESS were measured, and a bidding strategy optimization model was constructed to maximize profit. Subsequently, based on the environmental, social, and governance (ESG) concept, an integrated value model was constructed to evaluate the economics of ESS. Finally, the economics of ESSs were analyzed using the proposed model in a case study. The results indicate the following: (1) The TOU pricing model can effectively reduce the peak-to-valley load difference; (2) The integrated value of ESS is 249,930 yuan, and economic value of ESS is 22,016 yuan in scenarios 1, implying that the ESS has positive externalities under the TOU pricing mechanism; and (3) The net profit and economics of ESS are positively correlated with the rated power of the storage battery and negatively correlated with its rated capacity.

Model of integrated pool/conventional/alternative electricity market operation using pay-as-bid pricing

A pricing model considering the simultaneous interaction of pool, conventional and alternative markets in a power system is presented. This work inserts a competition for alternative power plants separating the pricing for these three markets. The model use the ‘Pay-as-Bid’ (PAB) pricing approach in opposite of classical marginal pricing (MP) approach because the inelastic demand characteristic and the necessity of reduce total operation cost. In the model pricing approach, an integration process involves an AC Optimal Power Flow (OPF) for obtaining awarded bids in the pool and market with the presence of conventional or alternative energy bilateral contracts. As a result, the obtained prices of pool, conventional and alternative energy incorporate the influences of the topology, voltage levels, losses and capacity limits of generators and transmission lines. Results show that agents can plan their portfolios based on reasonable stable prices that reflect the impact of supplying several electricity types and associated costs of resources in several operation scenarios and bid strategies. From the perspective of the system regulator, the minimization of payments by PAB ensures the supply of energy, transmission losses and alternative energy requirements as well as enforcing financial adequacy. Numerical cases are presented for evaluating the model

Wind Power Bidding Based on an Ensemble Differential Evolution Algorithm with a Problem-Specific Constraint-Handling Technique

The intermittent nature of wind power generation induces great challenges for power bidding in the electricity market. The deployment of battery energy storage can improve flexibility for power bidding. This paper investigates an optimal power bidding strategy for a wind–storage hybrid power plant in the day-ahead electricity market. To handle the challenges of the uncertainties of wind power generation and electricity prices, the optimal bidding problem is formulated as a risk-aware scenario-based stochastic programming, in which a number of scenarios are generated using a copula-based approach to represent the uncertainties. These scenarios consider the temporal correlation of wind power generation and electricity prices between consecutive time intervals. In the stochastic programming, a more practical but nonlinear battery operation cost function is considered, which leads to a nonlinear constrained optimization problem. To solve the nonlinear constrained optimization problem, an ensemble differential evolution (EDE) algorithm is proposed, which makes use of the merits of an ensemble of mutant operators to generate mutant vectors. Moreover, a problem-specific constraint-handling technique is developed. To validate the effectiveness of the proposed EDE algorithm, it is compared with state-of-the-art DE-based algorithms for constrained optimization problems, including a constrained composite DE (C2oDE) algorithm and a novel DE (NDE) algorithm. The experimental results demonstrate that the EDE algorithm is much more reliable and much faster in finding a better bidding strategy against benchmarking algorithms. More precisely, the average values of the success rate are 0.893, 0.667, and 0.96 for C2oDE, NDE, and EDE, respectively. Compared to C2oDE and NDE, the average value of the mean number of function evaluations to succeed with EDE is reduced by 76% and 59%, respectively.

Online transfer learning-based residential demand response potential forecasting for load aggregator

Accurate demand response (DR) potential forecasting is the basis for load aggregators (LA) to make optimal bidding strategies in DR market trading. LAs usually face practical challenges when they perform forecasts for those new customers who have no historical response data. Transfer learning provides a promising solution to this problem by leveraging knowledge acquired from other existing contracted customers. However, traditional transfer learning methods are trained offline and cannot make use of the latest response information of these new customers, which may result in large forecasting errors since the response behavior of new customers usually dynamically changes. To address the above issues, this paper proposes an online transfer learning-based DR potential forecasting framework, in which two forecasting models are established. The first one is built using the historical data of existing customers and this model is then transferred to the target domain (i.e., new customers) by parameter sharing and fine-tuning. The second model is built using the local response data of new customers, which gradually accumulates with the increasing participation of DR events. These two models are combined by an adaptive ensemble framework based on their online performances, thus enabling it to dynamically track the changes in new customers' response behavior. Case studies on a real-world dataset validate the effectiveness of the proposed framework.

Day-ahead bidding and dispatch strategy of novel battery charging and swapping station under multiple uncertainties

The novel battery charging and swapping station (NBCSS) has great operational flexibility due to its integration of wind power, photovoltaic power, gas turbine and energy storage. This paper presents a day-ahead bidding and dispatch strategy of NBCSS under multiple uncertainties, which consists of battery demand, market price, renewable energy and load. Firstly, the optimized backup method is used to address the uncertainty of battery demand. Then, a two-stage stochastic robust optimization model is further applied to address the uncertainties of market price, renewable energy and load. The model’s goal is to minimize the total operational cost in the worst-case scenario, which is limited by the corresponding uncertainty set. Especially, to control the conservatism of the model, the total adjustable budget and temporal adjustable budget of uncertainties are considered together. Next, the two-stage model is solved efficiently based on the strong duality theory and column-and-constraint generation (C&CG) method. Finally, case studies are given to verify the effectiveness and performance of the proposed model. The results demonstrate that the NBCSS can be efficiently deployed, enabling market arbitrage and reducing the overall cost. Additionally, the conservatism of the proposed model can be adjusted according to the budgets of uncertainties.

Comparative analysis of bidding strategies for auction-based local energy markets

In view of the ongoing integration of distributed energy resources (DERs) and energy storage into the energy system, conventional consumers are transitioning into prosumers and flexumers. Local energy markets (LEMs) enables these end users to trade electricity directly with each other in order to obtain lower energy prices and to increase the local self-consumption. Since bidding strategies have a decisive impact on efficient trading in auction-based LEMs, the comparison and consistent evaluation of bidding strategies is an important research area. As novelty we propose an uniform evaluation methodology keeping all market features equal except the used bidding strategy. For the first time we evaluate a device-oriented (DO) strategy including an incentive price signal with a zero-intelligence (ZI) strategy, a learning-intelligence (LI) strategy employing the modified Roth–Erev learning algorithm. As evaluation we analyze the resulting market trading with various key performance indicators (KPIs), which consider both cost savings and local energy supply. Our findings reveal that the chosen bidding strategy has a decisive impact on cost savings and the distribution of gains between the buyer and seller side. In a photovoltaic (PV) and combined heat and power (CHP) scenario with different technology penetrations, the end users’ gains attain the highest values for the DO bidding strategy with 7909 € and 16454 €. Also the average market clearing prices are for the DO bidding strategy the highest with 0.269 €/kWh for the and 0.3032 €/kWh, which implies that the seller side predominantly obtains the gains.

Research on Power Investment and Expansion Planning Considering Coal-fired Power Capacity and Electricity Price in Market-Driven Environment

The market-oriented environment and the shift away from the coal-fired power are inevitable trends in the future development of electric power energy. Based on this, this article establishes a dual-level model for power investment expansion planning with wind power system in an oligopoly competitive electricity market. In the upper-level model, power generation companies make investment decisions and formulate their bidding strategies in market transactions, while the lower-level model simulates the day-ahead market clearing process under the centralized bidding and trading mode. To encourage the transition from coal-fired power plants, a model for expected annual operating revenue of coal-fired power companies under a dual pricing system is proposed, and the conditional value-at-risk (CVaR) is used to measure the investment risk brought by the uncertainty of competitors in the investment planning game for power generation companies. The numerical results verify the effectiveness of the proposed model and method.

Deep Multi-Task Multi-Agent Reinforcement Learning Based Joint Bidding and Pricing Strategy of Price-Maker Load Serving Entity

Deep Multi-Task Multi-Agent Reinforcement Learning Based Joint Bidding and Pricing Strategy of Price-Maker Load Serving Entity

Real-Time Bidding Strategy of Energy Storage in an Energy Market with Carbon Emission Allocation Based on Aumann-Shapley Prices

Real-Time Bidding Strategy of Energy Storage in an Energy Market with Carbon Emission Allocation Based on Aumann-Shapley Prices

A study of coupled incentive-based clearing mechanisms in electricity markets considering uncertainty risk

Abstract Promoting the construction of the electricity spot market and considering the market-oriented trading mode with joint participation of the generation side and load side and fair competition will be the trend of future development of the electricity spot market. The unit generation bidding strategy is adjusted by this paper based on incentive factors and an incentive-based clearing mechanism is designed for market coupling. To establish a coupled two-layer model of the power market, the upper layer of the heterogeneous generator bidding strategy modeling, the lower layer of the incentive-based market clearing mechanism modeling. On this basis, a spot market model is designed in which both the generation and user sides participate in peaking, and the peaking resources on the generation side and the user side are included in the market mechanism bidding. This paper’s clearing cost can save 854.42 yuan compared to the unified clearing mechanism in the bidding stage for clearing the total market electricity energy volume of 1000kW. The total carbon emissions are reduced by 5816.8 tons in a single day when compared to the single electric energy trading mechanism, which is based on the market-coupled incentive system. The market model proposed in the paper can effectively improve the level of new energy consumption and provide a useful reference for the design of the electricity market in regions with a high proportion of new energy.

Data-driven model for real-time evaluating capacity of commercial buildings to participate in the demand response

The collective building stock is a major energy consumer, driving political discussions and forcing building owners to seek out cost-effective technologies. This creates a business opportunity in demand response (DR) services for commercial real estate. Despite recent efforts, developing a simple, scalable model remains an open question. We develop a real-time method for estimating electricity consumption profiles and inertia, enabling a bidding strategy on the DR market without compromising indoor climate. Accurate prediction of temperature changes is achieved through efficient models validated with real data.

Non-Cooperative Bidding Game Strategies of Power Generation Companies in the Electricity-Carbon Market Environment

This research scrutinizes the non-cooperative bidding strategies of power generation firms within the integrated electricity and carbon market framework. It aims to furnish pivotal insights for market strategy formulation and underpin policy development toward a greener power sector. Key innovations include a non-cooperative game model elucidating the interplay and competitive conduct among power providers in dual markets, alongside an empirical analysis showcasing how strategic bidding influences economic and environmental outcomes. This study significantly contributes to understanding market dynamics in promoting sustainable and environmentally sound power systems.

Risk-Averse Optimal Combining Forecasts for Renewable Energy Trading Under CVaR Assessment of Forecast Errors

Renewable energy producer is often exposed to huge financial losses in some imbalance hours (meaning that the contracted energy in day-ahead market is not equal to the actual output in real time) caused by extremely large forecast error. To address this challenge, this paper integrates the forecast end-user's risk profile into the development of risk-averse combining forecast approach for renewable energy trading. First, the conditional value-at-risk (CVaR) is applied to evaluate the extreme prediction error of combined forecasts. Then, convex optimization models are formulated with the objective of minimizing the mean square error plus the CVaR of large error. Solving our proposed models determines the optimal weights for individual models participating in the combined forecasts. Finally, the value of risk-averse combined forecasts is verified through examining the financial performance of using risk-averse forecasts as inputs of the bidding strategy in renewable energy trading. Case studies on real-world datasets present that our proposed method not only reduces the mean error but also lowers the extreme error. More importantly, it decreases the imbalance energy and cost in renewable energy trading, thus being less exposed to the risk of large financial losses under extreme prediction errors.

Dominance Constraints for Risk Control of a VPP’s Optimal Bidding Strategy

Dominance Constraints for Risk Control of a VPP’s Optimal Bidding Strategy

Research on Bidding Model of Electricity Selling Company Considering Green Certificate Trading and Electricity Selling Blocking

This article considers the optimal bidding model of traditional thermal power generation companies’ electricity sales companies, taking into account transmission congestion, under the influence of new policies such as the renewable portfolio standard (RPS) and the renewable exchange certificate (REC). Firstly, the transaction cost of green certificates is included in the cost of electricity sales, and an optimization model is established with the goal of maximizing the profit of the electricity sales company; Secondly, the node electricity price method is used to manage transmission congestion, and an optimization model is established with the goal of minimizing the cost of transmission congestion; Finally, multi agent technology and dynamic game theory are used to solve the optimal bidding strategy of the electricity selling company.

Optimal Bidding Strategy for PV and BESSs in Joint Energy and Frequency Regulation Markets Considering Carbon Reduction Benefits

Optimal Bidding Strategy for PV and BESSs in Joint Energy and Frequency Regulation Markets Considering Carbon Reduction Benefits