Bidding Model Research Articles

Equilibrium Model of Electricity Market Bidding Considering Incentive-based Demand Response

In the initial stage of electricity market construction, incentive-based demand response can effectively mitigate price spikes. Considering the overall equilibria of the market, it is of great significance to study the impact of incentive-based demand response on market clearing and participants’ behaviours. Firstly, this paper analyzes the compensation methods of incentive- based demand response and establishes an equilibrium model of day-ahead electricity market bidding that takes into account demand response. Secondly, considering the overall equilibrium conditions of the market, the overall equilibrium model of the market is established. Results show that the incentive-based demand response mechanism has a restraining effect on the generators’ bidding, and the load reduction effect is prominent when the network is congested.

Research on bidding mechanism and strategy of hydropower unit based on coupling resources

The construction of the electric power spot market in China is at an initial stage, and there is a contradiction between the bidding mode of single-unit bidding and the physical operating characteristics of cascade generating units. When all generators in the market make price declarations and settlements independently, the dispatching and clearing results may violate the operating performance of the actual units, bring difficulties to the unit joint bidding, and at the same time cause serious deviations in scheduling and waste of actual resources. Therefore, for the power generation resources coupled with resources such as cascade hydropower units, this paper proposes a bidding mechanism based on aggregation nodes. First, the bidding mechanism and bidding strategy of the aggregation node is introduced in detail, and then the spot market-clearing model of the aggregation node bidding is established. Finally, the IEEE5 simulation shows that the bidding mechanism based on the aggregation node can make the market-clearing result reflect the unit’s willingness to trade, effectively allocate cascade hydropower generation resources, and improve market transaction efficiency.

Bi-level strategic bidding model for P2G facilities considering a carbon emission trading scheme-embedded LMP and wind power uncertainty

As an emerging energy storage technology for renewable power utilization, power-to-gas (P2G) requires efficient bidding strategies to increase profit. This paper proposes a bi-level strategic bidding model for P2G with the consideration of a carbon emission trading scheme (CETS), an embedded locational marginal price (LMP) and wind power uncertainty, where the upper level model aims to maximize the profit of P2G facilities and the lower level model carries out the market clearing process. Under a CETS, a P2G facility purchases electricity power according to the LMP and is assumed to sell synthetic natural gas (SNG) and carbon emission permits for revenue. The electricity market clearing process is represented by a stochastic low carbon economic dispatch (ED) model considering the CETS, P2G and wind power uncertainty. On this basis, the formulation of a CETS embedded LMP (CETS-LMP) is presented and analyzed here. The bi-level model is reformulated and converted into mixed-integer linear programming (MILP) using strong duality theory. Case studies performed on a modified PJM 5-bus system and an IEEE 118-bus system verify the effectiveness of the proposed model and demonstrate that considering the CETS can increase the profit of P2G facilities and reduce carbon emission.

Bargaining Game-Based Profit Allocation of Virtual Power Plant in Frequency Regulation Market Considering Battery Cycle Life

Distributed energy resources (DERs) such as rooftop photovoltaic (PV) systems, battery energy storage systems (BESSs), and controllable loads can be aggregated as virtual power plants (VPPs) to provide frequency regulation services for managing power system stability. Optimizing the profits of PV-BESS VPPs in frequency control markets can demonstrate their profitability and encourage more PV-BESS consumers to join VPPs to support the grid. This article proposes an optimal bidding strategy of a PV-BESS VPP in frequency control ancillary services (FCAS) markets, and a joint bidding strategy of the VPP in cooperation with a wind farm, which aims to maximize their cooperation profits. Moreover, the battery cycle life is systematically considered and incorporated in the proposed bidding models. In addition, a payoff allocation approach based on Nash-Harsanyi Bargaining Solution is innovatively developed to allocate the extra profit of the cooperation. The proposed approach is expected to allocate the VPP with a reasonable share and reflect its real contribution in cooperation. The simulation results verify the feasibility and effectiveness of the proposed bidding models and the payoff allocation approach.

Security-Constrained generation and transmission expansion planning based on optimal bidding in the energy and reserve markets

The increasing growth of power system demand causes different challenges for the network operation and security situation. Therefore, this paper presents security-constrained generation and transmission expansion planning (SCGTEP) according to a strategic bidding model in the energy and reserve markets. This method includes a bi-level optimization problem, where its upper level refers to the transmission company (TransCo) and generation company (GenCo) economic model. This level minimizes the difference between TransCos and GenCos costs and revenues in the energy and reserve markets subjected to allowed investment budget for transmission lines and generation units. Moreover, the lower level problem considers the energy and reserve market-clearing model based on AC security-constrained optimal power flow. In other words, this problem minimizes the summation of network operation and security costs limited to the linearized AC optimal power flow equations, reserve formulation of generation units, load shedding model due to N – 1 contingency, and linearized voltage stability constraints. In the next step, the single-level SCGTEP is achieved using the Karush-Kuhn-Tucker, and it is solved by the Benders decomposition approach to obtain an optimal solution at a low calculation time. This strategy includes the uncertainties of load, renewable generation power, and the availability of the network equipment, hence, scenario-based stochastic programming is used in this paper to model uncertain parameters. The proposed SCGTEP is tested on the 6-bus and 118-bus IEEE networks in the GAMS software. According to the obtained numerical results, the proposed strategy can be simultaneously improved operation and security indices about 34.5% and 100%, respectively, compared to the power flow analysis based on the optimal location of generation units and transmission lines. Also, GenCos and TransCos can clear their investment cost after 5 years with participation in the electricity market.

Market trading model before the distribution network under the participation of multi-interest parties at the early stage of marketization

Along with the promotion of the electricity sales side market reform, load-side resources, including new energy sources, gradually participate in the distribution network electricity sales market. At the early stage of marketization, in order to give full participation rights to all parties involved in the market and to take into account the responsibility of distribution network operators to ensure the safety and quality of power supply, we propose a distribution network day-ahead market transaction model with the participation of multiple interests at the early stage of marketization. Firstly, a tripartite interest model including distribution network operators, distributed power supply operators and load aggregators is established in the distribution network, and all three parties take time-of-use tariff as the game strategy; secondly, a Pay as Bid (PAB) model is combined with the tripartite interest model to establish a market settlement model, and the three parties can modify their own offers according to the available information in the market settlement process. Finally, the Nash-Q method is used to solve the model. The results show that, compared with the traditional constant/time-sharing tariff, the model can ensure the safe and reliable power supply to customers while stimulating the active participation of new energy and other market players in the market, and can also increase the contribution of new energy to the power balance and reduce the risk of new energy consumption in the distribution network.

A Coordinated Bidding Model for Wind Plant and Compressed Air Energy Storage Systems in the Energy and Ancillary Service Markets Using a Distributionally Robust Optimization Approach

Clean energy resources, like wind, have a stochastic nature, which involves uncertainties in the power system. Introducing energy storage systems (ESS) to the network can compensate for the uncertainty in wind plant output and allow the plant to participate in ancillary service markets. Advance in compressed air energy storage system (CAES) technologies and their fast response make them suitable for ancillary services. This paper investigates the participation of a combined energy system composed of wind plants and compressed air energy storage system (CAES) in the energy market from a private owner’s viewpoint, including trading in energy markets and bidding for frequency regulation and reserve capacity in ancillary service markets. Since this problem contains various uncertainties associated with market prices, wind generation levels, and regulation signals, distributionally robust optimization (DRO) is used to model the uncertainties and enhance the simultaneous participation of a combined wind-CAES system in day-ahead energy and ancillary service markets. This method combines the advantages of stochastic and robust optimization. In contrast to robust optimization (RO), the method consolidates specific statistical data to reduce conservative results. Simulation results demonstrate the proposed model’s effectiveness in handling uncertainties and provide a framework for investors in this area. In addition, case study analyses are applied to assess the model’s performance and validate the coordination of a wind plant and compressed air energy storage system in participating in a deregulated electricity market. Finally, DRO and RO are compared in modeling the uncertainties of the optimization problem. The optimal outputs demonstrate the effectiveness of DRO in terms of achieving higher realized profits with less conservative results.

Research on Two-stage Game Strategy of Virtual Power Plant in Deep Peak Regulation Auxiliary Service Market

There is a large number of combined heat and power units in northern China, and due to the limit of heating demand, the operating mode of setting electricity by heat of combined heat and power units has seriously took over the consumption space of other energy, resulting in severe wind power curtailment and rationing situation in some areas, so this paper studies the deep peak regulation bidding strategy problem considering multiple uncertainties on virtual power plants, and established a two-staged optimization model of virtual power plant to maximize the net revenue, then introduced the Shapely value method with correction coefficient redistribute the peak regulation revenue. The simulation results showed that the two-stage bidding model can not only improve the market competitiveness of the virtual power plant, but also promote the consumption of renewable energy and reduce the market peak regulation service cost. Meanwhile, the improved apportion method can effectively guarantee the enthusiasm of all kinds of units to participate in the deep peak regulation market.

Bidding Strategy of a Flexible CHP Plant for Participating in the Day-Ahead Energy and Downregulation Service Market

To integrate renewable energy generation from wind and solar radiation, most combined heat and power (CHP) plants in northern China are undergoing large-scale upgrades and retrofitting to improve operational flexibility. Simultaneously, the downregulation service (DRS) market is still retained in the day-ahead market in many provinces. Considering a flexible CHP plant with multiple types of coal-fired CHP units and heat storage as a price taker, this paper establishes a collaborative bidding strategy for the CHP plant to participate in both the day-ahead energy market and day-ahead DRS market. The bidding strategy includes the optimal output decision model, power output range model, capacity segmenting model, and bidding models for two markets determined by the marginal generation cost (MGC) and the marginal downregulation cost (MDRC), respectively. These models are validated based on realistic data from a CHP plant in Northeast China.

Bounded rationality based multi-VPP trading in local energy market: A dynamic game approach with different trading targets

It is expected that multiple virtual power plants (multi-VPPs) will appear and participate in the future local energy market (LEM). The trading behaviors of those VPPs need to be carefully studied in order to maximize the benefit brought to the local energy market operator (LEMO) and each VPP. we propose a bounded rationality based trading model of multi-VPPs in local energy market by using a dynamic game approach with different trading targets. Three kinds of power bidding models for VPPs are first set up with different trading targets. In the dynamic game process, VPPs can also improve the degree of rationality and then find the most suitable target at different requirements by evolutionary learning after considering the opponents' bidding strategies and its own clustered resources. LEMO would decide the electricity buying/selling price in the LEM. Furthermore, the proposed dynamic game model is solved by a hybrid method consisting of the improved particle swarm optimization (IPSO) algorithm and conventional large-scale optimization. Finally, case studies are conducted to show the performance of the proposed model and solution approach, which may provide some insights for VPPs to participate in the LEM in real-world complex scenarios.

Optimal Operation Strategy of Virtual Power Plant Considering Real-Time Dispatch Uncertainty of Distributed Energy Resource Aggregation

Virtual power plant (VPP) technologies continue to develop to embrace various types of distributed energy resources (DERs) that have inherent real-time uncertainty. To prevent side effects on the power system owing to the uncertainty, the VPP should manage its internal resources’ uncertainty as a whole. This paper proposes an optimal operation strategy for a VPP participating in day-ahead and real-time energy market so that a distributed energy resource aggregation (DERA) can cope with real-time fluctuation due to uncertainties while achieving its maximum profit. The proposed approach has bidding models of the DERAs including microgrid, electric vehicle aggregation, and demand response aggregation, as well as the VPP. The VPP determines internal prices applied to the DERA by evaluating its real-time responses to the day-ahead schedule and updating proposed pricing function parameters, and the DERA adjusts its energy reserves. By repeating this coordination process, the VPP can establish an optimal operation strategy to manage real-time uncertainty on the DERA’s own. The effectiveness of the proposed strategy is verified by identifying a capability of the DERA to cope with real-time fluctuation through scenario-based simulations. The result shows that the VPP can reduce 1.6% of cost while the internal price applied to the DERA is close to the maximum.

Individual Thermal Generator and Battery Storage Bidding Strategies Based on Robust Optimization

Bidding in the day-ahead market encompasses uncertainty on market prices. To properly address this issue, dedicated optimal bidding models are constructed. Traditionally, these models have been derived for generating units, in particular thermal generators. Recently, optimal bidding models have been updated to account for specifics of energy storage, foremost battery storage. Batteries are significantly different devices than generators. On one hand, a battery can both purchase and sell electricity with practically instant change in its output power. On the other hand, a battery is energy-limited, which makes its profit very sensitive to optimal scheduling. In this paper, we examine the existing and derive new robust optimization-based optimal bidding models individually for a thermal generator and a battery storage. The models are examined in terms of the expected profit by applying the obtained bidding curves and (dis)charging schedules to actual realizations of uncertainty. Moreover, we examine the effect of the range of uncertainty caused by the selection of input scenarios. Based on the presented case studies, we form conclusions on the effectiveness of the robust optimization approach for this type of problems.

Comparison of flexible bidding and hourly bidding in the electricity market

Flexible bidding is an emerging electricity trading product in the Nordic spot market, which is different from hourly bidding and block bidding. It provides electricity generation enterprises and users with flexible and changeable electricity generation and consumption options and can reflect the electricity generation and demand-side behavior and habits of electricity generation and consumption, providing reference value for Chinese market participants to participate in the electricity market and demand-side response. This paper firstly introduces the two bidding models of hourly bidding and flexible block bidding; secondly, it introduces the market clearance model of flexible bidding; finally, it compares the differences between these two bidding approach.

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.

Resource Allocation in Cloud using Multi Bidding Model with User Centric Behavior Analysis

Background: Number of resource allocation and bidding schemes had been enormously arrived for on demand supply scheme of cloud services. But accessing and presenting the Cloud services depending on the reputation would not produce fair result in cloud computing. Since the cloud users not only looking for the efficient services but in major they look towards the cost. So here there is a way of introducing the bidding option system that includes efficient user centric behavior analysis model to render the cloud services and resource allocation with low cost. Objective: The allocation of resources is not flexible and dynamic for the users in the recent days. This gave me the key idea and generated as a problem statement for my proposed work. Methods: An online auction framework that ensures multi bidding mechanism which utilizes user centric behavioral analysis to produce the efficient and reliable usage of cloud resources according to the user choice. Results: we implement Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis. Thus the algorithm is implemented and system is designed in such a way to provide better allocation of cloud resources which ensures bidding and user behavior. Conclusion: Thus the algorithm Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis is implemented & system is designed in such a way to provide better allocation of cloud resources which ensures bidding, user behavior. The user bid data is trained accordingly such that to produce efficient resource utilization. Further the work can be taken towards data analytics and prediction of user behavior while allocating the cloud resources.

Hyperpath Truck Routing in an Online Freight Exchange Platform

Online freight exchange (OFEX) platforms serve the purpose of matching demand and supply for freight in real time. This paper studies a truck routing problem that aims to leverage the power of an OFEX platform. The OFEX routing problem is formulated as a Markov decision problem, which we solve by finding the bidding strategy at each possible location and time along the route that maximizes the expected profit. At the core of the OFEX routing problem is a combined pricing and bidding model that simultaneously (1) considers the probability of winning a load at a given bid price and current market competition, (2) anticipates the future profit corresponding to the current decision, and (3) prioritizes the bidding order among possible load options. Results from numerical experiments constructed using real-world data from a Chinese OFEX platform indicate that the proposed routing model could (1) improve a truck’s expected profit substantially, compared with the benchmark solutions built to represent the state of the practice, and (2) enhance the robustness of the overall profitability against the impact of market competition and spatial variations.

Day Ahead Bidding of a Load Aggregator Considering Residential Consumers Demand Response Uncertainty Modeling

As the electricity consumption and controllability of residential consumers are gradually increasing, demand response (DR) potentials of residential consumers are increasing among the demand side resources. Since the electricity consumption level of individual households is low, residents’ flexible load resources can participate in demand side bidding through the integration of load aggregator (LA). However, there is uncertainty in residential consumers’ participation in DR. The LA has to face the risk that residents may refuse to participate in DR. In addition, demand side competition mechanism requires the LA to formulate reasonable bidding strategies to obtain the maximum profit. Accordingly, this paper focuses on how the LA formulate the optimal bidding strategy considering the uncertainty of residents’ participation in DR. Firstly, the physical models of flexible loads are established to evaluate the ideal DR potential. On this basis, to quantify the uncertainty of the residential consumers, this paper uses a fuzzy system to construct a model to evaluate the residents’ willingness to participate in DR. Then, based on the queuing method, a bidding decision-making model considering the uncertainty is constructed to maximize the LA’s income. Finally, based on a case simulation of a residential community, the results show that compared with the conventional bidding strategy, the optimal bidding model considering the residents’ willingness can reduce the response cost of the LA and increase the LA’s income.

Bidding Strategy for Virtual Power Plant Considering the Large-Scale Integrations of Electric Vehicles

In order to alleviate the energy crisis and environmental pollution, electric vehicles (EVs) have been developed on a large-scale throughout the world in the exceptional period. On the other hand, virtual power plant (VPP), an aggregator involved in various renewable energies and diversified loads, is considered as the crucial measure to coordinate with the EVs to participate in the power market. Obviously, the equivalent model of EV in the VPP is difficult to fulfill the need for large-scale development of EVs in the future. Therefore, it is of great significance to establish the detailed charging models of EVs and explore the influences on the operation of the VPP. First, the basic structure and operation feature of VPP considering four different kinds of EVs is introduced briefly. Moreover, in order to describe the bidding processing of VPP, the three stages of power market are introduced from the trading aspects. Then, the charging characteristics of four types of EVs are analyzed in terms of charging time, charging power, and access probability. In addition, the bidding strategy models of VPP at three different stages are built and the improved Artificial Bee Colony algorithm is utilized to solve the optimal bidding strategies of the VPP. Finally, the performances of the bidding model of VPP are validated by the different scenarios that considered the multitype EVs integrations, and the influences of the different scales of EVs on the bidding strategy of VPP are also analyzed.

Accurate Modeling of a Profit-Driven Power to Hydrogen and Methane Plant Toward Strategic Bidding Within Multi-Type Markets

A power to hydrogen and methane (P2HM) plant can couple three different energy sectors including electricity, natural gas, and hydrogen, showing great potentials in supporting the penetration of renewable energy sources. This article develops a bi-level strategic bidding model of a profit-driven P2HM plant from both market and technology perspectives. In the upper-level model, the P2HM plant submits day-ahead bids of energy prices and quantities to the power, natural gas, and hydrogen markets, respectively, with the objective to maximize its benefit; Moreover, the technical problems for operating this P2HM plant are also considered. In the lower-level model, multi-type markets are handled using different approaches based on market characteristics, to achieve an appropriate framework for multi-type markets. Specifically, the power and natural gas markets are characterized by the widely used optimal power and gas flows (OPGF) with network constraints, and the quantity-based competition Cournot model is employed to represent the emerging hydrogen market. In model solving, the bi-level optimization model is reformulated into a mathematical program with equilibrium constraints (MPEC) model. Subsequently, the strong-duality theory is applied to linearize the nonlinear terms of the model. Two case studies are performed to show the effectiveness of this model.

A bi-level model for optimal bidding of a multi-carrier technical virtual power plant in energy markets

Distributed energy resources (DERs) play an important role in the future vision of distribution energy networks. This paper with the aim of promoting DERs role in energy markets proposes a new framework for the optimal bidding strategy of a Technical Virtual Power Plant (TVPP) in different markets. In the proposed framework, it is assumed that the TVPP energy power is distributed throughout a multi-carrier energy network (MCEN) which consists of district heat networks (DHNs) and a distributed electricity network (DEN). In the MCEN, CHP units are the linkage between DHNs and DEN. With the goal of maximizing the profit of TVPP in different local and wholesale energy markets, a bi-level optimization model is formularized to set scheduling of DERs. As the upper level, the TVPP sets the schedule of its DERs considering technical constraints of DHNs and DENs. In the lower level, however, the TVPP runs an optimization problem to estimate the energy price in wholesale energy markets considering the forecasted bids of other rivals. The proposed model is implemented on a test system and the results confirms that the local market creates an opportunity for CHP owner, TVPP and the heat consumers to make more profit as well as achieving better technical performance for distribution energy systems.