Marginal Electricity Prices Research Articles

Analysis and Calculation of Marginal Electricity Price of Nodes with Network Loss from the Perspective of Intelligent Robot Considering Digital Signal Processing Technology

Analysis and Calculation of Marginal Electricity Price of Nodes with Network Loss from the Perspective of Intelligent Robot Considering Digital Signal Processing Technology

Electricity price forecast in wholesale markets using conformal prediction: Case study in Mexico

AbstractIn recent years, machine and deep learning models have attracted significant attention for electricity price forecast in global wholesale electricity markets. Yet, a predominant focus on point forecast in most parts of literature limits the practical application of these models due to the absence of uncertainty quantification. In this study, we first perform an analysis of the electricity price trends in the Mexican wholesale electricity market to determine the influence of key variables. Using independent component analysis and wavelet coherence analysis, we were able to identify primary determinants influencing locational marginal electricity prices. Subsequently, we applied four different models covering the most important algorithms proposed in the literature for electricity price forecast. Our findings revealed that the most accurate forecasting results were achieved using a deep learning‐based method with a decision tree‐based model trailing closely. Finally, we incorporate conformal prediction for uncertainty quantification by calculating the prediction intervals with a target coverage level of 95%. The conformal prediction intervals provide a more comprehensive view of the possible future scenarios, enhancing economic efficiency, risk management, and decision‐making processes. This is particularly important because of the dynamic nature of electricity markets, where prices are strongly influenced by multiple factors.

Reserve provision of combined-cycle unit in joint day-ahead energy and reserve markets

With the continuous development of natural gas-fired combined cycle power generation technology, the combined cycle (CC) unit's installed capacity is gradually increasing in the power system because of its flexible operation characteristics. Participating in the energy and reserve markets and taking advantage of flexible mode transfer characteristics, the CC unit can provide the system with energy and sufficient reserve capacity. This paper proposes a bi-level strategic bidding model to study the CC unit’s bidding behavior in the day-ahead energy and reserve markets. The upper-level model is an optimal bidding model for the configuration-based CC unit aiming to maximize its profit. The lower-level model is the joint market clearing model of the energy and reserve markets managed by the independent system operator (ISO). CC unit and ISO in this model influence each other by the locational marginal electricity prices (LMEP) and the locational marginal reserve prices (LMRP) obtained from the lower-level market-clearing model. The CC unit submits the bids to the ISO and arranges the generation plan according to the market clearing results of the lower-level market-clearing model carried out by the ISO. The proposed bi-level optimization model is transformed into a mathematical equilibrium constraint (MPEC) problem by Karush-Kuhn-Tucker (KKT) conditions and duality theory. In this paper, the improved IEEE-6 bus and IEEE-57 bus systems are used as case studies to verify the effectiveness of the proposed bidding strategy for the CC unit. The results show that the proposed bidding strategy can effectively improve the profits of the CC unit in the electricity market. Compared with the case of the CC unit only participating in the energy market, the profits of the CC unit in the market increased by 8.84% and 12.3%.

Marginal utility of battery energy storage capacity for power system economic dispatch

The battery energy storage (BES) is recognized as a key resource for the power fluctuations smoothing, peak load shaving and frequency regulation, and its performance depends heavily on the available capacity. It is meaningful to investigate the influence of the BES capacity on the power system operation cost. This paper focuses on the marginal utility of BES capacity in the day-ahead power system operation cost. Firstly, the day-ahead economic dispatch problem is formulated as a constrained quadratic programming problem, with the consideration of the BES degradation cost. Then, the relationship between the BES-related shadow prices and the marginal electricity price is derived from the KKT conditions of the dispatch problem. Based on it, the marginal utility of BES capacity is modeled to evaluate the effect of BES capacity on the system day-ahead operation cost. Finally, the numerical results demonstrate that the proposed marginal utility can manifest the influence of BSS capacity on the power system operation.

Frequency dynamics-aware real-time marginal pricing of electricity

This paper presents a frequency dynamics-aware marginal pricing method for real-time electricity markets. The proposed method captures the impact of system frequency dynamics on the marginal price of electricity in the presence of aggressive net-load variations made more likely by greater renewable integration. We formulate a dynamics-aware economic dispatch (ED) by augmenting the traditional single-snapshot ED with constraints pertaining to system frequency dynamics, and frequency deviations from the synchronous speed are penalized in the modified objective function. The resulting ED embeds two distinct time steps to optimize over fast system dynamics and slower decisions on generator set-points. We prove that the dynamics-aware marginal price is the (suitably scaled) Lagrange multiplier of the power balance constraint and that it converges in steady state to the marginal price obtained from traditional ED solved with comparable system load. Numerical case studies involving the Western System Coordinating Council test system validate our findings and confirm added revenue opportunities for generators contributing to frequency regulation.

Forecasting of locational marginal price components with artificial intelligence and sensitivity analysis: A study under tropical weather and renewable power for the Mexican Southeast

Electricity price forecasting is fundamental for energy market orientation, providing information contributing to decision making in the short term. Electricity prices are affected by the seasonal, environmental, operational, and regional factors that introduce complex nonlinearities to the prediction process. This work presents a novel hybrid approach based on artificial intelligence and global sensitivity analysis (GSA) to determine the degree to which these external factors influence the components that make up the locational marginal price (LMP) of electricity. The proposed approach is applied for the day-ahead market conditions of the Mexican southeast, a region with a tropical climate having substandard transmission capacity. The study considered a database composed of 11 independent variables (integrated by seasonal, environmental, operational, and economic factors) to forecast the energy, loss, and congestion components presented in the LMP. The data were subjected to preprocessing consisting of filters for cleaning, smoothing, and normalization. Subsequently, a multi-output model based on artificial neural networks (ANN) was trained. In the end, a global sensitivity analysis (GSA) was performed, which quantified the impact of inputs on products as well as the interactions among the inputs. The optimal model has a 50–50–50 architecture, which resulted in a forecast accuracy of over 90% for energy and losses components. GSA results show a strong relationship between the input variables and electricity prices, highlighting that the components are not affected in the same way by the exogenous inputs. The day of the year was the variable with the most significant impact on the loss and congestion components. The aforementioned is associated with the electricity transmission capacity deficit. On the other hand, fuel prices significantly impact the energy component, which is related to the generation mix profile of the peninsular region. The new hybrid methodology would improve the activities of researchers, energy companies, modelers, and electricity market forecasters, such as planning, energy management, and decision-making for policymakers and electricity traders.

INVESTIGATION OF RENEWABLE ENERGY USE IN THE P2X TECHNOLOGY

Objective: The electricity-to-hydrogen technology can convert surplus renewable energy electric energy into chemical energy. Hydrogen plays an important role in transportation, power generation, and other fields. Therefore, developing electrochemical (P2X) technology for renewable energy consumption effectively solves renewable energy curtailment. Methods: The four aspects of market scale, technical route, energy conversion efficiency, and demonstration project progress are reviewed, and the energy efficiency of the four electrochemical technologies is compared, Power consumption, marginal electricity price, equivalent output, and market share five major technical and economic indicators. To analyze the strengths, weaknesses, opportunities, and threats of P2X in China, a literature review survey was conducted, relying on recent two-decade publications from four main publishers: Scopus, Springer, Wiley, and Taylor and Francis. Keywords were selected from the first-hand references based on their impact on P2X or related topics listed in the literature databases. The keywords as Power to X, Power to chemicals, PtX, and P2X were chosen according to their actual involvement or keen interest in P2X projects. Results: The research results based on the low-temperature electrolysis technology route show that the comprehensive energy efficiency of the electricity-to-methane and electricity-to-gasoline technologies is higher (50%); the electricity-to-gasoline technology is the most economical (marginal electricity price is 0.37 yuan/kWh), but the synthesis process requires carbon monoxide and carbon emissions, And the technical risk is high; the promotion of electricity to ammonia will have the greatest impact on the market (17.18%). Reducing coal consumption by about 22.85 million tons and the environmental protection significance of electricity-to-ammonia conversion (reducing carbon emissions by about 39.1 million tons) are two important directions for future electrochemical technology. Conclusion: Facing the development of P2X technology in the future, the plan and economics of the high-temperature electrical and chemical technology route based on high-temperature solid oxides were preliminary discussed and prospected.

On the role of present bias and biased price beliefs in household energy consumption

The intermittency of energy billing may give rise to different biases or misperceptions, such that household energy demand deviates from its true optimum. This study investigates such deviations by linking variation in present-biased preferences and energy price beliefs to variation in energy consumed. Using both a survey and incentivized experiments, we gather measures of present bias as well as price beliefs, and observe participants’ true electricity consumption. Our main finding is that participants with present bias are predicted to consume on average 9 to 10 percent more electricity than participants with time-consistent discounting. Our results further suggest that neither the true marginal electricity price nor the perceived marginal electricity price can predict electricity consumption. These findings raise doubt as to the effectiveness of classic price-based policies in reducing household energy consumption.

A Wind Energy Supplier Bidding Strategy Using Combined EGA-Inspired HPSOIFA Optimizer and Deep Learning Predictor

As the integration of large-scale wind energy is increasing into the electricity grids, the role of wind energy suppliers should be investigated as a price-maker as their participation would influence the locational marginal price (LMP) of electricity. The existing bidding strategies for a wind energy supplier faces limitations with respect to the potential cooperation, other competitors’ bidding behavior, network loss, and uncertainty of wind production (WP) and balancing market price (BMP). Hence, to solve these problems, a novel bidding strategy (BS) for a wind power supplier as a price-maker has been proposed in this paper. The new algorithm, called the evolutionary game approach (EGA) inspired hybrid particle swarm optimization and improved firefly algorithm (HPSOIFA), has been proposed to handle the bidding issue. The bidding behavior of power suppliers, including conventional power suppliers, has been encoded as one species to obtain the equilibrium where the EGA can explore dynamically reasonable behavior changes of the opponents. Each species of behavior change has been exploited by the HPSOIFA to improve the optimization solutions. Moreover, a deep learning algorithm, namely deep belief network, has been implemented for improving the accuracy of the forecasting results considering the WP and BMP, and the uncertainty revealed in the WP and BMP has been modeled by quantile regression (QR). Finally, the Shapley value (SV) has been calculated to estimate the benefits of cooperative power suppliers. The presented case studies have verified that the proposed algorithm and the established bidding strategy exhibit higher effectiveness.

Game analysis of decreasing generation cost characteristics in electricity spot market

The construction of Chinese electricity spot market is in its infancy. Market rules have borrowed from foreign mature market experiences, adopted marginal electricity price settlement mechanisms, and stipulated that the biddings on the power generation side will increase monotonously. However, there are thermal power generating units in China whose marginal cost does not meet the characteristics of monotonous increase. In this context, studying the strategic behaviour of thermal power generating units with diminishing marginal cost characteristics in the market, highlighting the impact of existing rules on market participants’ decision making, is of great significance to the design and operation of Chinese electricity spot market. First, an existing generation market clearing model based on monotonically increasing quotes and a strategy model for generating units based on monotonically decreasing marginal costs are established. Then, the above models are organically combined to form a bilevel optimization model, and the iterative optimization is performed to obtain a market equilibrium solution. Finally, a numerical example is used to clearly describe the bidding strategy of the unit in the spot market, and the incentive effects of market regulations on its bidding behaviour are discussed.

Probabilistic evaluations on marginal price and capacity adequacy of power systems with price-elastic demand

An increasing deployment of smart grid technologies and demand response programs has enabled demands to be more elastic in response to electricity prices, actively contributing to power system operation. However, demand-price elasticity could potentially impact accuracy of electricity price forecasting and generation capacity adequacy evaluation, which have not been thoroughly explored in literature. To this end, a probabilistic model is developed to evaluate marginal electricity price and capacity adequacy of power systems with elastic demand, while considering uncertainties of both demand and supply sides (e.g., random outages of generating units with multi-bidding-segments as well as variability of renewable energy outputs). Specifically, based on the concept of marginal bidding segment, the probability of a bidding segment being marginal is computed via the probabilistic production simulation approach. Accordingly, the discrete probability distribution of marginal electricity price can be obtained, which is further used to calculate mean and variance of marginal electricity price with elastic demand. Moreover, considering the interaction between elastic demands and uncertain electricity prices, the loss-of-load probability and the expected unserved energy are further estimated. Numerical studies are presented to validate effectiveness of the proposed method.

Electricity Price Distributions in Future Renewables-Dominant Power Grids and Policy Implications

Future electricity systems with tight constraints on carbon emissions will rely much more on wind and solar generation, with zero marginal cost, than today. We use capacity expansion modelling of Texas in 2050 to illustrate wholesale price distributions in future energy-only, carbon-constrained grids without price caps under a range of technology/system assumptions. Tightening carbon emissions constraints dramatically increases the frequency of very low prices. The frequency of high prices also increases, and all resources earn the bulk of their energy market revenues in relatively few hours. The presence of demand response, long-duration energy storage, dispatchable low-carbon generation, or a robust market for hydrogen for non-electricity use (and for energy storage) weakens but does not undo these results. Financial instruments to hedge price volatility will consequently be more costly and it is likely that we will need to redesign capacity remuneration mechanisms to provide adequate incentives for optimal investment in VRE generation and, particularly, storage. In order to encourage economy-wide electrification, the marginal retail price of electricity should be low whenever the wholesale price is low. With automated control of demand via demand response contracts, the risks of price volatility faced by retail customers can be mitigated without sacrificing efficiency. To encourage economy-wide electrification, the marginal retail price of electricity should be low when the wholesale spot price is low. We discuss ways of reducing consumers’ risk in this world while providing adequate investment incentives.

The effect of an electricity tax on aggregate electricity consumption: evidence from Basel

We estimate the effect of an electricity tax on aggregate electricity consumption with the synthetic control method. The tax was introduced in the Swiss city of Basel in 1999 and, together with other tariff changes, increased marginal electricity prices by 5.4–8.0%. We compare the actual and a hypothetical electricity consumption in the years 1999–2006. The latter is a weighted average of electricity consumption in other Swiss cities and captures the hypothetical situation without the tax. We find a statistically insignificant effect of the tax increase of − 2.7 to − 1.9%, which implies a rather small, but not unreasonable, price elasticity of between − 0.5 and − 0.2. Ambiguous effects on average prices and an unfortunate communication by officials may explain why the innovative reform failed to induce a stronger response.

Social Versus Private Benefits of Energy Efficiency Under Time-of-Use and Increasing Block Pricing

Many energy policies are implemented to subsidize the adoption of energy efficiency. However, when private benefits from energy efficiency exceed the social benefits, there is an incentive for the consumers to over-invest in energy efficiency; otherwise, there is an incentive to under-invest. This study adds to this discussion by providing an empirical estimation of the electricity savings and social benefits after energy efficiency retrofits for consumers on time-of-use (TOU) and increasing block pricing, respectively. We aim to examine how social versus private savings from a given energy efficiency measure may be different depending on different pricing plans. This study applies hourly electricity data for about 16,000 residential consumers during 2013–2017 in Arizona. We show that for the TOU consumers, the private savings from energy-efficient AC retrofits are greater than the social savings by 61%, while the increasing block rate consumers’ private savings exceed the social savings by 46%, when other market failures are not considered (e.g., principal-agent problem and imperfect information). Different rate plans impose different marginal electricity prices which influence the incentives to invest in energy efficiency as well as electricity consumption behaviors that can influence both the private and social savings from energy efficiency. The result indicates that there should be potentially different levels of policy interventions towards energy efficiency for consumers on different pricing. Additionally, we also find that energy efficiency makes the electricity demand more elastic to price changes.

Utilities Included: Split Incentives in Commercial Electricity Contracts

This paper quantifies a tenant-side “split incentives” problem that exists when the largest commercial sector customers are on electricity-included property lease contracts, causing them to face a marginal electricity price of zero. We use exogenous variation in weather shocks to show that the largest firms on tenant-paid contracts use up to 14 percent less electricity in response to summer temperature fluctuations. The result is retrieved under weaker identifying assumptions than previous split incentives papers, and is robust when exposed to several opportunities to fail. The electricity reduction in response to temperature increases is likely to be a lower bound when generalized nationwide and suggests that policymakers should consider a sub-metering policy to expose the largest commercial tenants to the prevailing retail electricity price.

A Novel Market Clearing and Safety Checking Method for Multi-Type Units That Considers Flexible Loads

Flexible loads have flexibility and variability in time and space, and they have been widely studied by scholars. However, the research on the participation of flexible loads in market clearing and safety checking is still insufficient. We propose a market clearing and safety checking method for multi-type units that considers flexible loads. First, the flexible load is divided into reducible loads, shiftable loads, and convertible loads, and its mathematical model is established. Then, the convertible loads are considered in the market clearing model, and the power management agency executes the market clearing procedure to obtain the clearing result. When the line power exceeds the limit as a result of clearing, the power flow of the branches and sections is eliminated by adjusting the unit output and reducing the flexible load at the same time, and a safety checking model considering load reduction is established. The marginal electricity price of the nodes is obtained by the interior point method, and we solve the model by calling the CPLEX (v12.7.1) solver in GAMS (General Algebraic Modeling System v24.9.1). We use a regional power grid of 220 kV and above as an example for analysis; the results show that the proposed method can reduce the marginal electricity price of the nodes, reduce the cost of safety checking, and improve the safety of the market clearing.

A Two-Stage Algorithm of Locational Marginal Price Calculation Subject to Carbon Emission Allowance

To analyze the effect of carbon emission quota allocation on the locational marginal price (LMP) of day-ahead electricity markets, this paper proposes a two-stage algorithm. For the first stage of the algorithm, a multi-objective optimization model is established to simultaneously minimize the total costs and carbon emission costs of power systems. Hence, an evenly distributed Pareto optimal solution can be solved effectively by means of the normalized normal constraint method. For the second stage, a tracing model is built with the goal of minimizing the total costs of power systems and satisfying the constraints generated based on the Pareto optimal solution obtained from the first stage. Furthermore, the influence of carbon emission quota allocation on the LMP of electricity markets is analyzed, and different schemes to allocate carbon emission quotas are evaluated on a real 1560-bus and 52-unit system.

Distributionally Robust Scheduling of Integrated Gas-Electricity Systems With Demand Response

This paper proposes a distributionally robust scheduling model for the integrated gas-electricity system (IGES) with electricity and gas load uncertainties, and further studies the impact of integrated gas-electricity demand response (DR) on energy market clearing, as well as locational marginal electricity and gas prices (LMEPs and LMGPs). The proposed model maximizes the base-case system social welfare (i.e., revenue from price-sensitive DR loads minus energy production cost) minus the worst-case expected load shedding cost. Price-based gas-electricity DRs are formulated via price-sensitive demand bidding curves while considering DR participation levels and energy curtailment limits. By linearizing nonlinear Weymouth gas flow equations via Taylor series expansion and further approximating recourse decisions as affine functions of uncertainty parameters, the formulation is cast into a mixed-integer linear programming problem to enhance computational tractability. Case studies illustrate effectiveness of the proposed model for ensuring system security against uncertainties, avoiding potential transmission congestions, and increasing financial stability of DR providers.

Continuous-Time Locational Marginal Price of Electricity

Modern electricity markets with large penetration of renewable energy resources require fair and accurate pricing methods to elicit generation flexibility and foster competition in electricity markets. This paper proposes the fundamental theory and closed-form formulas for continuous-time locational marginal price (LMP) of electricity, which more accurately integrates the spatio-temporal variations of load and operational constraints of power systems in the electricity price calculation. This paper first formulates the network-constrained generation scheduling and pricing problems as continuous-time optimal control problems using two methods for modeling transmission network, i.e., Theta and generation shift factor (GSF) methods. The continuous-time network-constrained scheduling and pricing problems minimize in their objective functional the total operation cost of power systems over the scheduling horizon subject to generation and transmission constraints. The closed-form continuous-time LMP formulas are derived for each transmission network model, which explicitly include terms that reflect the simultaneous spatio-temporal impacts of transmission flow limits and intertemporal generation ramping constraints in LMP formation. A scalable and computationally efficient function space solution method is proposed that converts the continuous-time problems into mixed integer linear programming problems with finite-dimensional decision space. The proposed solution method enables high-fidelity solution of transmission-constrained scheduling and pricing problems in higher dimensional spaces, while including as a special case the current discrete-time solutions. The proposed models are implemented on a three-bus system and the IEEE reliability test system, where the proposed models showcase more accuracy in reflecting the impacts of fast net-load variations over discrete-time counterparts.

Utilities Included: Split Incentives in Commercial Electricity Contracts

This paper quantifies a tenant-side “split incentives” problem that exists when the largest commercial sector customers are on electricity-included property lease contracts causing them to face a marginal electricity price of zero. We use exogenous variation in weather shocks to show that the largest firms on tenant-paid contracts use up to 14 percent less electricity in response to summer temperature fluctuations. The result is retrieved under weaker identifying assumptions than previous split incentives papers, and is robust when exposed to several opportunities to fail. The electricity reduction in response to temperature increases is likely to be a lower bound when generalized nationwide and suggests that policymakers should consider a sub-metering policy to expose the largest commercial tenants to the prevailing retail electricity price.