Market-clearing Model Research Articles

Cyber-Vulnerability Analysis for Real-Time Power Market Operation

With the rapid advancement and integration of communication and sensor technologies, power system operation is becoming more vulnerable to cyberattacks, particularly attacks in which malicious data could induce catastrophic consequences on market operations. Financial risks, as well as the potential physical damages, raise growing concerns about the reliable operation of the electricity market. Existing market-targeting cybersecurity research has focused on developing attack strategies or detection schemes. However, the lack of cyber-vulnerability analysis (CVA) hinders operators from systematically evaluating the real-time (RT) market-clearing model and identifying potential threats from a cybersecurity perspective. This article proposes a comprehensive CVA model for delivering a detailed analysis of four aspects of vulnerability: highly probable cyberattack targets, devastating attack targets, risky load levels, and mitigation ability under different degrees of defense. Users can simulate interactions between attackers and defending operators under different attack events, and the corresponding market settlements are also obtained. The proposed bilevel model is recast into mixed-integer linear programming through Karush-Kuhn-Tucker (KKT) conditions. A simulation study on an IEEE-30 bus system demonstrates the accuracy and effectiveness of the proposed CVA model.

Energy Pricing Mechanism Considering Energy Converter Devices as Components of Pan-Energy Transmission System

Price plays a vital role in developing well-functioning multi-carrier systems. Conventional single energy markets set prices for each specific type of energy carrier. Their prices are correlated due to energy converter devices, but the relations are obscure. In the unified energy market (UEM), a more efficient and sufficient pricing mechanism can be achieved with energy converter devices as components of the pan-energy transmission system. Thus, we create a unified market framework for multi-energy systems, including gas system, heat system and electricity system. Take the integrated gas and power system, for example. We analytically derived the interrelation between natural gas prices and electricity prices based on locational marginal price (LMP) theory. And the energy conversion capacity constraints binding will result in LMPs diversity. As a result, a new market-clearing model for the unified market of electricity and gas is proposed, where gas-fired generators are no longer electricity production competitors but the energy transmission service provider. Numerical results verify the quantitative relations between clearing prices of gas and electricity, and how energy conversion capacity binding impact the price relationships. It also demonstrates that the proposed method can increase social welfare and improve market dispatch efficiency.

A Stochastic Distribution System Market Clearing and Settlement Model With Distributed Renewable Energy Utilization Constraints

The increasing integration of distributed generation (DG) has changed the way modern power systems are planned and operated. However, new market mechanisms at the retail level are still needed to effectively integrate energy resources and emerging market participants in the distribution grid. In this article, a stochastic market clearing and settlement model with distributed renewable energy (DRE) utilization constraints is presented. The proposed model considers the uncertainties associated with the DRE productions in the distribution grid and ensures that a certain portion of the total available DRE at a specified time will be utilized with a high probability. The problem is formulated as a chance-constrained two-stage stochastic programming model that minimizes the expected distribution system operation cost and is solved through a sample average approximation algorithm. Case studies are conducted to verify the effectiveness of the proposed model for different DRE utilization requirements.

Coordinated P2P electricity trading model with aggregated alliance and reserve purchasing for hedging the risk of deviation penalty

The increasing of distributed energy and flexible load incentivize market participants to participate in a more active market. A coordinated peer to peer (P2P) trading model with an aggregated alliance and reserve purchasing is proposed in this paper. Under such a coordinated trading model, the market participants form an alliance, where the agents perform aggregated alliance and purchase reserve from a mobile energy storage supplier. To reduce the risk of deviation penalty in P2P trading, the agents seek to maximize the welfare of the entire alliance in the P2P process, while considering product differentiation and deviation risks. The proposed trading model design comprises: (i) a coordinated P2P market design with the aggregated alliance and reserve purchasing, (ii) a two-stage P2P market-clearing model to maximize trading utility while reducing the risk of deviation. To solve this two-stage multivariable coupling problem in a distributed way, we propose a primal–dual based ADMM method. Through the case study, compared with the traditional P2P trading model, the proposed market model can reduce the deviation penalty and improve the comprehensive welfare, while the convergence can be effectively guaranteed.

A clearing mechanism for joint energy and ancillary services in non-convex markets considering high penetration of renewable energy sources

This paper introduces a day-ahead clearing model for the simultaneous energy and ancillary services market in high penetration of renewable energy sources (RESs) considering fast-start generators’ behaviors as non-spinning reserve providers. For managing the uncertainties of RESs, the system operator takes the advantages of the stochastic model of security-constrained unit commitment (SCUC) problem considering plausible scenarios. The non-convexities due to the startup costs, minimum power outputs, and commitment variables make the stochastic SCUC problem and market-clearing non-convex. Meanwhile, with traditional market-clearing methods for energy and ancillary services based on marginal costs or dual variables, the net profit of some generators may become negative. In this situation, more financial losses will be imposed on fast-start generators since they are marginal/intermittent units for managing the uncertainties of RESs. Using a duality gap minimization and profit insurance, this paper presents a pricing model to solve these issues. The proposed market-clearing model leads to uniform prices for ancillary services and nodal prices for energy guaranteeing the non-negative net profit of all generators. The results of the 3-buses system and IEEE 118-buses system are analyzed to illustrate the applications of the proposed pricing approach.

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.

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.

Review of Research on Electric Energy Market Clearing Model

In recent years, the construction of China’s electricity market has achieved many results, and the market transaction volume and the proportion of clean energy consumption have increased significantly. With the expansion of the market size and the scope of the main player, the establishment of a clearing model for the electric energy market has been fully studied. This paper first introduces the current research status of foreign clearing models, and then according to the development trend of the electric energy market in the process of new electricity reformation, it summarizes and combs the electricity market clearing models under various market members and different scenarios by domestic scholars in recent years. Finally, it puts forward suggestions and prospects for the research of the future market clearing model, hoping to provide reference for future research.

Characteristics of locational uncertainty marginal price for correlated uncertainties of variable renewable generation and demands

With the rapid increase of variable renewable energy sources in power systems, how to manage and price the uncertainty of renewable resources’ power outputs is becoming an urgent issue. Current market designs considering the uncertainties are mainly based on the probabilistic scenario set of demand and renewable energy resources power outputs. This consideration makes market designs vulnerable to three significant challenges when put into practice. First, the accurate probability distribution of renewable generation is hard to obtain in real-time. Second, it is challenging to clear the market timely with many scenarios to guarantee accuracy. Third, generation cost recovery cannot be guaranteed for some scenarios. To overcome these challenges, this paper proposes a locational uncertainty marginal price model to price the uncertainty explicitly based on a scenario-free stochastic market-clearing model. Instead of using the probabilistic scenario set, the uncertainty of renewable energy sources and loads is modeled with distributionally-robust chance constraints. The correlation of uncertainties can be endogenously modeled in both the market-clearing and the locational uncertainty marginal price formation. Furthermore, this paper proves that generation cost recovery, revenue adequacy, and partial market equilibrium can be achieved using the locational uncertainty marginal price model. Numerical results from both the small and large systems simulations validate that the generation cost recovery is maintained no matter the generation participates in uncertainty mitigation or not. The transmission congestion surplus is also allocated appropriately among loads, renewable energy sources, and financial transmission right owners.

Reliability Based Reactive Power and Reserve Market Modeling Considering Uncertainty on Units Outages

A separate reactive market is necessary due to the vital role of reactive power in the system security and reliability. In order to maintain the reliability of the system, reactive power-based indices should be utilized in the reactive power market-clearing model, in which the improvement of these indices depends on the amount of reactive power reserve availability in the system. Therefore, the reactive power and reactive reserve markets have been modeled in this paper for the first time taking into account the reactive power-based reliability indices. In the present paper, a new stochastic framework for day-ahead reactive power and reserve markets clearing is formulated based on the reliability. A two-stage stochastic programming model is presented for the reactive power market clearing process, in which generation uncertainties have been simulated by the Monte Carlo method. In the stochastic programming model, a set of scenarios are considered for the stochastic reactive power market clearing. In each optimization problem, the objective function is defined as the total payment function (TPF) of generators. TPF includes the amount paid to the generators in order to compensate for their reactive power and reactive reserve. The efficiency of the proposed model is tested on IEEE 24-bus reliability system (IEEE RTS).

A robust distributed market-clearing model for multi-area power systems

The importance of cross-border electricity trading is common knowledge in the modern energy markets. As proved in different studies, a centralized market structure has the most efficiency in the profit maximization point of view. However, it is not possible to make a centralized decision-making structure, owing to the privacy of data for each area, and the large-scale problem should be solved. In this paper, a new market-clearing scheme is proposed for multi-area power systems, including both traditional and renewable power generation units based on the Optimality Condition Decomposition (OCD) approach. The proposed framework needs neither coordination nor monitoring entities and uses a small number of data that should be interchanged between the areas. Also, a Single-Stage Robust Programming method is used to tackle the general uncertainties of the large-scale power systems, such as load consumption profiles and wind units’ generations. Finally, the proposed structure is tested through simulating on a standard three-area test case, and the efficacy, adequacy, and accuracy of the results are confirmed using various analyses.

Risk Trading in a Chance-Constrained Stochastic Electricity Market

Existing electricity market designs assume risk neutrality and lack risk-hedging instruments, which leads to suboptimal market outcomes and reduces the overall market efficiency. This paper enables risk-trading in the chance-constrained stochastic electricity market by introducing Arrow-Debreu Securities (ADS) and derives a risk-averse market-clearing model with risk trading. To enable risk trading, the probability space of underlying uncertainty is discretized in a finite number of outcomes, which makes it possible to design practical risk contracts and to produce energy, balancing reserve and risk prices. Notably, although risk contracts are discrete, the model preserves the continuity of chance constraints. The case study illustrates the usefulness of the proposed risk-averse chance-constrained electricity market with risk trading.

Coordinated Market Design for Peer-to-Peer Energy Trade and Ancillary Services in Distribution Grids

A novel peer-to-peer (P2P) market design is proposed in this work for the distribution grid level. Envisioning that the grid constraints violations are the major challenge for P2P energy sharing, we propose these to be handled through the ancillary service (AS) market. By calculating the decomposable distribution locational marginal prices (DLMPs), the essential price signals of procuring ASs can be recovered to determine the grid usage prices (GUPs) to each P2P transaction. Hence, the GUPs, due to their decomposable properties, act as incentive signals for the P2P market to support the grid operation in terms of loss reduction, voltage support and congestion management. The proposed market design comprises i) an interactive market design of P2P trade & AS and, ii) a fully distributed peer-centric market-clearing model for P2P energy trade. The duality analysis provides the composition of market equilibrium prices of P2P trading and their interpretations. The case studies demonstrate the effectiveness of the proposed P2P trade to support grid operational objectives.

Two-Stage Robust-Stochastic Electricity Market Clearing Considering Mobile Energy Storage in Rail Transportation

This paper proposes a two-stage robust-stochastic framework to evaluate the effect of the battery-based energy storage transport (BEST) system in a day-ahead market-clearing model. The model integrates the energy market-clearing process with a train routing problem, where a time-space network is used to describe the limitations of the rail transport network (RTN). Likewise, a price-sensitive shiftable (PSS) demand bidding approach is applied to increase the flexibility of the power grid operation and reduce carbon emissions in the system. The main objective of the proposed model is to determine the optimal hourly location, charge/discharge scheduling of the BEST system, power dispatch of thermal units, flexible loads scheduling as well as finding the locational marginal price (LMP) considering the daily carbon emission limit of thermal units. The proposed two-stage framework allows the market operator to differentiate between the risk level of all existing uncertainties and achieve a more flexible decision-making model. The operator can modify the conservatism degree of the market-clearing using a non-probabilistic method based on info-gap decision theory (IGDT), to reduce the effect of wind power fluctuations in real-time. In contrast, a risk-neutral-based stochastic technique is used to meet power demand uncertainty. The results of the proposed mixed-integer linear programming (MILP) problem, confirm the potential of BEST and PSS demand in decreasing the LMP, line congestion, carbon emission, and daily operation cost.

Power trading region considering long-term contract for interconnected power networks

The identification of an exact power trading region is crucial for the optimal utilization of power resources among interconnected regional networks. However, existing studies generally ignore the link between the long-term and day-ahead electricity markets, which threatens the secure and economic operation of power systems. This paper proposes a method to identify the unified power trading region in the day-ahead electricity market precisely, considering the long-term power contract. This unified power trading region describes the feasible regions of both tie-line power transmission and power contract execution. Furthermore, the source and sink regional networks can be considered uniformly by the proposed power trading region. All day-ahead and long-term contract constraints are preserved via multi-parametric programming analysis. Moreover, the influence of the long-term contract in the day-ahead market is revealed from a new perspective. On this basis, a non-iterative decentralized multi-area market-clearing model is proposed that is compatible with the existing decentralized clearing framework. Finally, the exact convex relaxation formulation of the proposed multi-area market-clearing model is derived.

A day-ahead electricity market-clearing model considering medium- and long-term transactions and wind producer participation

A transaction and settlement model of day-ahead markets that considers both long-term contract and wind power producer participation is proposed for Chinas electricity market, in which current developments promote both long-term contracts and spot transactions, and in which a trend to improve new energy accommodation through the spot market exists. The combination of long-term contract electricity and day-ahead transactions, as well as a joint optimization model of electric power and reserve, has been proposed. A multi-scenario probability distribution was used to examine the stochastic nature of wind generation. The case studies demonstrate the effectiveness and rationality of the proposed model by comparison with the sequential clearing model. The results provide an analytical tool for the transition from the current electricity market, which consists mainly of long-term contracts and spot transactions.

Multi-Objective Market Clearing Model with an Autonomous Demand Response Scheme

Demand response (DR) is known as a key solution in modern power systems and electricity markets for mitigating wind power uncertainties. However, effective incorporation of DR into power system operation scheduling needs knowledge of the price–elastic demand curve that relies on several factors such as estimation of a customer’s elasticity as well as their participation level in DR programs. To overcome this challenge, this paper proposes a novel autonomous DR scheme without prediction of the price–elastic demand curve so that the DR providers apply their selected load profiles ranked in the high priority to the independent system operator (ISO). The energy and reserve markets clearing procedures have been run by using a multi-objective decision-making framework. In fact, its objective function includes the operation cost and the customer’s disutility based on the final individual load profile for each DR provider. A two-stage stochastic model is implemented to solve this scheduling problem, which is a mixed-integer linear programming approach. The presented approach is tested on a modified IEEE 24-bus system. The performance of the proposed model is successfully evaluated from economic, technical and wind power integration aspects from the ISO viewpoint.

Distribution Locational Marginal Pricing (DLMP) for Congestion Management and Voltage Support

In this paper, a day-ahead market-clearing model for smart distribution systems is proposed. Various types of distributed energy resources (DERs), such as distributed energy storage, distributed generators, microgrids, and load aggregators, can bid into the day-ahead distribution-level electricity market. Considering system Volt/VAR control, network reconfiguration, and interactions with the wholesale market, an optimization model is built to clear the day-ahead market, through which the distribution locational marginal pricing (DLMPs) for both active power and reactive power are determined. Through derivations of the Lagrangian function and sensitivity factors, DLMPs are decomposed to five components (i.e., marginal costs for active power, reactive power, congestion, voltage support, and loss), which provide price signals to motivate DERs to contribute to congestion management and voltage support. Finally, case studies demonstrate the effectiveness of the proposed method.

A Stochastic Market Design With Revenue Adequacy and Cost Recovery by Scenario: Benefits and Costs

Two desirable properties of electricity market mechanisms include: 1) revenue adequacy for the market, and 2) cost recovery for all generators. Previously proposed stochastic market-clearing mechanisms satisfy both properties in expectation only, or satisfy one property by scenario and another in expectation. Consequently, market parties may perceive significant risks to participating in the market since they may lose money in one or more scenarios, and therefore be discouraged from offering in the market or perhaps even from investing. We develop a stochastic two-stage market-clearing model including day-ahead and real-time settlements with an energy-only pricing scheme that ensures both properties by scenario. However, this approach is cost-inefficient in general and may sacrifice other desirable market attributes. Undesirable consequences include: One group of participants will have to pay more to ensure that all other participants have their costs covered, and thus their prices will not be equilibrium supporting; and day-ahead and real-time prices are not arbitraged in expectation, although this can be fixed by allowing virtual bidders to arbitrage but at the potential cost of increased market inefficiency. Considering these pros and cons, we propose our model as an appropriate tool for market analysis, and not for clearing actual markets. Numerical results from case studies illustrate the benefits and costs of the proposed stochastic market design.

Is Being Flexible Advantageous for Demands?

This paper analyzes the impacts of flexible demands on day-ahead market outcomes in a system with significant wind power production. We use a two-stage stochastic market-clearing model, where the first stage represents the day-ahead market and the second stage represents the real-time operation. On one hand, flexibility of demands is beneficial to the system as a whole since such flexibility reduces the operation cost, but on the other hand, shifts in demands from peak periods to off-peak periods may influence prices in such a way that demands may not be willing to provide flexibility. Specifically, we investigate the impacts of different degree of demand flexibility on day-ahead prices. A number of scenarios modeling the uncertainty associated with wind production at the operation stage, and nonconvexities due to start-up costs of generators and their minimum power outputs are taken into account.