Locational Marginal Pricing Mechanism Research Articles

Bilateral bidding strategy in joint day-ahead energy and reserve electricity markets considering techno-economic-environmental measures

In this study, a new bilateral equilibrium model was developed for the optimal bidding strategy of both price-taker generation companies (GenCos) and distribution companies (DisCos) that participate in a joint day-ahead energy and reserve electricity market. This model, from a new perspective, simultaneously takes into account such techno-economic-environmental measures as market power, security constraints, and environmental and loss considerations. The mathematical formulation of this new model, therefore, falls into a nonlinear, two-level optimization problem. The upper-level problem maximizes the quadratic profit functions of the GenCos and DisCos under incomplete information and passes the obtained optimal bidding strategies to the lower-level problem that clears a joint day-ahead energy and reserve electricity market. A locational marginal pricing mechanism was also considered for settling the electricity market. To solve this newly developed model, a competent multi-computational-stage, multi-dimensional, multiple-homogeneous enhanced melody search algorithm (MMM-EMSA), referred to as a symphony orchestra search algorithm (SOSA), was employed. Case studies using the IEEE 118-bus test system—a part of the American electrical power grid in the Midwestern U.S.—are provided in this paper in order to illustrate the effectiveness and capability of the model on a large-scale power grid. According to the simulation results, several conclusions can be drawn when comparing the unilateral bidding strategy: the competition among GenCos and DisCos facilitates; the improved performance of the electricity market; mitigation of the polluting atmospheric emission levels; and, the increase in total profits of the GenCos and DisCos.

Locational Marginal Pricing Mechanism for Uncertainty Management Based on Improved Multi-ellipsoidal Uncertainty Set

The large-scale integration of renewable energy sources (RESs) brings huge challenges to the power system. A cost-effective reserve deployment and uncertainty pricing mechanism are critical to deal with the uncertainty and variability of RES. To this end, this paper proposes a novel locational marginal pricing mechanism in day-ahead market for managing uncertainties from RES. Firstly, an improved multi-ellipsoidal uncertainty set (IMEUS) considering the temporal correlation and conditional correlation of wind power forecasting is formulated to better capture the uncertainty of wind power. The dimension of each ellipsoidal subset is optimized based on a comprehensive evaluation index to reduce the invalid region without large loss of modeling accuracy, so as to reduce the conservatism. Then, an IMEUS-based robust unit commitment (RUC) model and a robust economic dispatch (RED) model are established for the day-ahead market clearing. Both the reserve cost and ramping constraints are considered in the overall dispatch process. Furthermore, based on the Langrangian function of the RED model, a new locational marginal pricing mechanism is developed. The uncertainty locational marginal price (ULMP) is introduced to charge the RES for its uncertainties and reward the generators who provide reserve to mitigate uncertainties. The new pricing mechanism can provide effective price signals to incentivize the uncertainty management in the day-ahead market. Finally, the effectiveness of the proposed mechanism is verified via numerous simulations on the PJM 5-bus system and IEEE 118-bus system.

A Structural Characterization of Market Power in Electric Power Networks

We consider a market in which capacity-constrained generators compete in scalar-parameterized supply functions to serve an inelastic demand spread throughout a transmission constrained power network. The market clears according to a locational marginal pricing mechanism, in which the independent system operator (ISO) determines the generators' production quantities to minimize the revealed cost of meeting demand, while ensuring that network transmission and generator capacity constraints are met. Under the stylizing assumption that both the ISO and generators choose their strategies simultaneously, we establish the existence of Nash equilibria for the underlying market, and derive an upper bound on the allocative efficiency loss at Nash equilibrium relative to the socially optimal level. We also characterize an upper bound on the markup of locational marginal prices at Nash equilibrium above their perfectly competitive levels. Of particular relevance to ex ante market power monitoring, these bounds reveal the role of certain market structures---specifically, the \emph{market share} and \emph{residual supply index} of a producer---in predicting the degree to which that producer is able to exercise market power to influence the market outcome to its advantage. Finally, restricting our attention to the simpler setting of a two-node power network, we provide a characterization of market structures under which a Braess-like paradox occurs due to the exercise of market power---that is to say, we provide a necessary and sufficient condition on market structure under which the strengthening of the network's transmission line capacity results in the (counterintuitive) increase in the total cost of generation at Nash equilibrium.

Core-Selecting Mechanisms in Electricity Markets

Due to its theoretical virtues, several recent works propose the use of the incentive-compatible Vickrey-Clarke-Groves (VCG) mechanism for electricity markets. Coalitions of participants, however, can influence the VCG outcome to obtain higher collective profit. To address this issue, we propose core-selecting mechanisms for their coalition-proofness. We show that core-selecting mechanisms generalize the economic rationale of the locational marginal pricing (LMP) mechanism. Namely, these mechanisms are the exact class of mechanisms that ensure the existence of a competitive equilibrium in linear/nonlinear prices. This implies that the LMP mechanism is also core-selecting, and hence coalition-proof. In contrast to the LMP mechanism, core-selecting mechanisms exist for a broad class of electricity markets, such as ones involving nonconvex costs and nonconvex constraint sets. In addition, they can approximate truthfulness without the price-taking assumption of the LMP mechanism. Finally, we show that they are also budget-balanced. Our results are verified with case studies based on optimal power flow test systems and the Swiss reserve market.

Energy Market Integration in ASEAN: Locational Marginal Pricing and Welfare Implications

Energy market integration (EMI) in the ASEAN through the ASEAN Power Grid (APG) is considered to improve the welfare of the economy. In the simulation analyses of cross-border power trading, charging only a tariff as a function of the distance transmitted as access costs appears to ignore the effect of a marginal change in demand or supply from consumers and generators, respectively, on the transmission grid. This, in turn, leads to a poor signal to the market, making suboptimal decisions by economic agents. This study aims to analyse how the locational marginal pricing (LMP) of transmission losses influences an optimal energy mix and energy trading in the ASEAN and derive policy implications for completing the APG. Energy trading with the LMP mechanism of transmission losses appears to provide benefits to the countries under the APG as the total cost of electricity generation declines when power trade increases among ASEAN countries. The underpinnings of positive results strongly suggest that ASEAN member countries seriously consider enhancing grid interconnection to realise the efficiency of power trading infrastructure. JEL Codes: F15, O13, Q49

Day-Ahead Schedule and Equilibrium for the Coupled Electricity and Natural Gas Markets

With the increasing utilization of natural gas-fired power plants, efficiency and even reliability challenges on both industries arise from the growing reliance on natural gas as fuel in the electricity sector and the increasing demand fluctuation in the natural gas sector. Following a brief review of the discontinuities, improvements are discussed to coping with the desired timely and cost-effectively gas supply in the electricity day-ahead market scheduling. Inspired by its successful implantation in the electricity market, the locational marginal pricing mechanism is applied to the natural gas day-ahead market. Moreover, the bidding strategy for general gas demand utilities is provided while considering the potential applicability of demand response (DR) which is also practiced in the electricity market. The formulations are represented as bilevel problems of each corresponding market, of which upper level problems maximize the participants’ profit, while the lower level problem renders the market clearing with schedules and locational marginal prices. Then, a coevolutionary algorithm is proposed to find the equilibrium of the two coupled markets with integrated participants. Results show that the proposed methodology with synchronized intraday time intervals and DR abilities could provide higher efficiency for the natural gas market, which will help the electricity market better coordinated to provide flatter price signals while scheduling more renewable energy sources.

An Efficient and Incentive Compatible Mechanism for Wholesale Electricity Markets

Being widely used in many deregulated wholesale electricity markets, the locational marginal pricing (LMP) mechanism is known to achieve social optimality in a competitive market. When profit-maximizing generators act strategically to manipulate prices; however, LMP may lead to high loss of economic efficiency. In this paper, we apply the Vickrey-Clarke–Groves (VCG) mechanism to wholesale electricity markets. We show that the VCG mechanism minimizes the total cost at a truth-telling dominant strategy equilibrium. We establish an important comparative result that the VCG mechanism always results in higher per-unit electricity prices than the LMP mechanism under any given set of reported supply curves. Numerical results show that the difference between the per-unit prices resulting from the two mechanisms is negligibly small (about 4%) in the IEEE 14-bus and 30-bus test systems. Finally, we apply the VCG mechanism to a day-ahead setting with start-up cost (of conventional generators) and intermittent renewable generation. We show that the VCG mechanism induces the truth-telling behavior of conventional generators in dominant strategies and yields each conventional generator a non-negative expected profit.

Dynamic Economic Dispatch Game: The Value of Storage

We formulate a dynamic economic dispatch game in which each generator has its own electricity storage device. The operation of storage introduces time-coupling constraints. We focus on how the use of storage may affect the market structure and market outcomes in the locational marginal pricing mechanism. We first show that even when the independent system operation is unaware of the storage, there exists an efficient bid profile that induces the optimal dispatch. We then demonstrate that the use of storage does not increase the room for strategic play, but may improve the equilibrium outcomes. We provide sufficient conditions under which there exist efficient Nash equilibria. Furthermore, we propose the marginal contribution pricing mechanism that guarantees efficient outcomes.

Price of Fairness on Networked Auctions

We consider an auction design problem under network flow constraints. We focus on pricing mechanisms that provide fair solutions, where fairness is defined in absolute and relative terms. The absolute fairness is equivalent to “no individual losses” assumption. The relative fairness can be verbalized as follows: no agent can be treated worse than any other in similar circumstances. Ensuring the fairness conditions makes only part of the social welfare available in the auction to be distributed on pure market rules. The rest of welfare must be distributed without market rules and constitutes the so-calledprice of fairness. We prove that there exists the minimum ofprice of fairnessand that it is achieved when uniform unconstrained market price is used as the base price. Theprice of fairnesstakes into account costs of forced offers and compensations for lost profits. The final payments can be different than locational marginal pricing. That means that the widely applied locational marginal pricing mechanism does not in general minimize theprice of fairness.

Agent-based modeling for trading wind power with uncertainty in the day-ahead wholesale electricity markets of single-sided auctions

This paper, for the first time, adopts agent-based simulation approach to investigate the bidding optimization of a wind generation company in the deregulated day-ahead electricity wholesale markets, by considering the effect of short-term forecasting accuracy of wind power generation. Two different wind penetration levels (12% and 24%) are investigated and compared. Based on MATPOWER 4.0 software package and the 9-bus 3-generator power system defined by Western System Coordinating Council, the agent-based models are built and run under the uniform price auction rule and locational marginal pricing mechanism. Each generation company could learn from its past experience and improves its day-ahead strategic offers by using Variant Roth–Erev reinforcement learning algorithm. The results clearly demonstrate that improving wind forecasting accuracy helps increase the net earnings of the wind generation company. Also, the wind generation company can further increase its net earnings with the adoption of learning algorithm. Besides, it is verified that increasing wind penetration level within the investigation range can help reduce the market clearing price. Furthermore, it is also demonstrated that agent-based simulation is a viable modeling tool which can provide realistic insights for the complex interactions among different market participants and various market factors.

Normal boundary intersection method for suppliers’ strategic bidding in electricity markets: An environmental/economic approach

In this paper the problem of developing optimal bidding strategies for the participants of oligopolistic energy markets is studied. Special attention is given to the impacts of suppliers’ emission of pollutants on their bidding strategies. The proposed methodology employs supply function equilibrium (SFE) model to represent the strategic behavior of each supplier and locational marginal pricing mechanism for the market clearing. The optimal bidding strategies are developed mathematically using a bilevel optimization problem where the upper-level subproblem maximizes individual supplier payoff and the lower-level subproblem solves the Independent System Operator’s market clearing problem. In order to solve market clearing mechanism the multiobjective optimal power flow is used with supplier emission of pollutants, as an extra objective, subject to the supplier physical constraints. This paper uses normal boundary intersection (NBI) approach for generating Pareto optimal set and then fuzzy decision making to select the best compromise solution. The developed algorithm is applied to an IEEE 30-bus test system. Numerical results demonstrate the potential and effectiveness of the proposed multiobjective approach to develop successful bidding strategies in those energy markets that minimize generation cost and emission of pollutants simultaneously.

Multiobjective environmental/techno-economic approach for strategic bidding in energy markets

This paper describes a method for developing optimal bidding strategy based on a bilevel optimization, considering suppliers’ emission of pollutants. The proposed methodology employs supply function equilibrium (SFE) model to represent the strategic behavior of each supplier. Locational marginal pricing mechanism is also assumed for settling the market and calculating the supplier profit. It is modeled as a bilevel optimization problem in which the upper-level subproblem maximizes individual supplier payoff and the lower-level subproblem solves the Independent System Operator’s market clearing problem. In this paper, the multiobjective optimal power flow is used to solve market clearing mechanism with supplier emission of pollutants, as extra objectives, subject to the transmission limits and supplier physical constraints. To illustrate the proposed approach under different conditions an IEEE 30-bus test system together with a number of case studies are used.