Real-time Market Prices Research Articles

A novel risk-averse optimal scheduling strategy for active distribution networks equipped with power-to-X technologies

This paper proposes a novel risk-averse approach for scheduling of an active electrical distribution network (AEDN). The network is connected to an electric vehicle parking lot (PL) and gas and hydrogen fueling stations (GHFSs). In addition, the AEDN operator is able to participate in both day-ahead and real-time markets to supply energy demands. In order to address the uncertainties of the studied network, a two-stage stochastic programming incorporating conditional value-at-risk (CVaR) is employed to model uncertainties of wind turbine and photovoltaic output, electric vehicles behavior in PL, and GHFSs energy consumption, while a robust optimization method is used to handle the uncertainty of real-time market price. According to the numerical results, the AEDN operator can decrease the risk level by up to 1.2% while the operational cost rises by 7.5%. Furthermore, the optimal use of multiple energy storage technologies and price-sensitive shiftable electric loads (PSELs) reduces the operational cost by 7.4%.

Planning with the electricity market One day ahead for a smart home connected to the RES by the MILP method

This study proposes a novel framework for smart homes to optimize energy consumption and production, leading to reduced costs and a more reliable grid. The framework schedules the use of controllable appliances and renewable energy sources while considering uncertainties in production, real-time market prices, and uncontrollable household loads. By incorporating both incremental and real-time pricing models, the system discourages excessive consumption during peak hours. The core innovation lies in a two-stage scheduling approach implemented using GAMS software. This method minimizes the expected total cost while accounting for limitations on controllable loads, power supply, production resources, battery performance, and overall home energy balance. Additionally, the framework leverages the previous day’s bilateral contract and allows residents to adjust desired lighting levels based on current market fluctuations. Simulations demonstrate the program’s effectiveness in reducing both net energy costs and peak load on the electricity grid.

Optimizing Energy Arbitrage: Benchmark Models for LFP Battery Dynamic Activation Costs in Reactive Balancing Market

This study introduces a novel benchmark model for lithium iron phosphate (LFP) batteries in reactive energy imbalance markets, filling a notable gap by incorporating comprehensive operational parameters and market dynamics that are overlooked by conventional models. Addressing the absence of a holistic benchmark for energy-storage systems in electricity markets, this research focuses on the integration of LFP batteries, considering their unique characteristics and market responsiveness. Regression and regularization techniques, coupled with temporal cross-validation, were employed to ensure model robustness and accuracy in predicting energy trading outcomes. This methodological approach allows for a nuanced analysis of battery degradation, power capacity, energy content, and real-time market prices. The model, validated using Belgium’s system imbalance market data from the 2020–2023 period, incorporates both capital and operational expenditures to assess the economic and operational viability of LFP battery energy-storage systems (BESSs). The findings reveal that considering a broader range of operational parameters in energy arbitrage, beyond just the usual energy prices and round-trip efficiency, significantly influences the cost-effectiveness and performance benchmarking of energy storage solutions. This paper advocates for the strategic use of LFP batteries in energy markets, highlighting their potential to enhance grid stability and energy trading profitability. The proposed benchmark model serves as a critical tool for energy traders, providing a detailed framework for informed decision making in the evolving landscape of energy storage technologies.

Two-stage distributionally robust offering and pricing strategy for a price-maker virtual power plant

This paper presents a two-stage distributionally robust approach for offering and pricing strategy of a virtual power plant (VPP) acting as a price-maker in the day-ahead market. The model incorporates uncertainties related to distributed photovoltaic (PV) output, real-time market price and charging stations demand. In the first stage, the VPP determines its day-ahead market bids/offers as a price maker and releases the retail price to the charging stations based on the worst case of probability distribution realizations. In the second stage, the VPP optimizes energy storage operations and the power transactions in the real-time market to maximize its revenue according to the optimal first stage decisions. To efficiently solve the two-stage model, a customized column-and-constraint generation algorithm (C&CG) is designed that decomposes non-convex subproblems into several small-scale linear programming problems that are independent of each other. The effectiveness of the proposed strategy is validated through case studies conducted with the IEEE 33-bus system.

Deep Reinforcement Learning-Based Spatiotemporal Decision of Utility-Scale Highway Portable Energy Storage Systems

Mobile charging is an efficient solution to meet peak charging demand on highways. In this paper we propose a deep reinforcement learning (DRL)-based approach to maximize the revenue of a utility-scale highway portable energy storage system (PESS) for on-demand electric vehicle charging. We consider a PESS that consists of an electric truck, battery, and charging stations on highways. Actions include the selection of stations and routes, the selection of charging and discharging, and the corresponding charging/discharging power selection. The first two are discrete, while the last is continuous. To deal with the hybrid action space and time-varying state space of PESS, we design an action space, observation space, and reward function according to its characteristics; in addition, we develop a state-of-the-art DRL model for online decision-making considering the uncertainty in the real-time market price of electricity. Through numerical simulations with real-world electricity price data in California, we demonstrate the effectiveness of the proposed method.

Predicting the Gap in the Day-Ahead and Real-Time Market Prices Leveraging Exogenous Weather Data

Predicting the price gap between the day-ahead Market (DAM) and the real-time Market (RTM) plays a vital role in the convergence bidding mechanism of Independent System Operators (ISOs) in wholesale electricity markets. This paper presents a model to predict the values of the price gap between the DAM and RTM using statistical machine learning algorithms and deep neural networks. In this paper, we seek to answer these questions: What will be the impact of predicting the DAM and RTM price gap directly on the prediction performance of learning methods? How can exogenous weather data affect the price gap prediction? In this paper, several exogenous features are collected, and the impacts of these features are examined to capture the best relations between the features and the target variable. An ensemble learning algorithm, namely the Random Forest (RF), is used to select the most important features. A Long Short-Term Memory (LSTM) network is used to capture long-term dependencies in predicting direct gap values between the markets stated. Moreover, the advantages of directly predicting the gap price rather than subtracting the price predictions of the DAM and RTM are shown. The presented results are based on the California Independent System Operator (CAISO)’s electricity market data for two years. The results show that direct gap prediction using exogenous weather features decreases the error of learning methods by 46%. Therefore, the presented method mitigates the prediction error of the price gap between the DAM and RTM. Thus, the convergence bidders can increase their profit, and the ISOs can tune their mechanism accordingly.

Stochastic two-stage coordination of electric vehicles in distribution networks: A multi-follower bi-level approach

As the number of electric vehicles (EVs) continues to rise, it is essential to consider appropriate management strategies for coordinating EVs connected to different buses in the power networks. In light of this, this paper proposes a stochastic two-stage bi-level model for coordinating EVs in a distribution network with charging stations under alternating current optimal power flow (ACOPF) constraints. The scheduling problem is considered to independently minimize the costs of the distribution system operator (DSO) and EVs parked at different charging stations located at various buses of the network. In the proposed model, the DSO as the leader, and all EVs as independent followers, are individual entities who try to follow their priorities and objectives. The amount and price of exchanged power between the DSO and EVs are optimally determined in the proposed model. The proposed bi-level model has been converted to a single-level model using the Karush–Kuhn–Tucker (KKT) conditions. Afterwards, the Big M method is used to convert the non-linear equations that appear due to utilizing the KKT approach. The scenario-based uncertainty modeling is used to model the uncertainty in input data such as day-ahead and real-time market prices, EVs’ initial state of charge (SOC), and arrival/departure time. The centralized unilateral form of the model has also been developed to validate the proposed model. The results indicate that the bi-level model can lead to cost reduction for the EVs.

A proactive strategy for virtual power plants including multiple private owners equipped with energy storages

This paper presents a novel framework for the optimal operation strategy of a virtual power plant (VPP) comprises a set of various stakeholders such as renewable energy sources, distributed energy resources with private owners, and flexible loads in day-ahead and real-time energy markets. In order to model the strategic behavior of private owners within the VPP geographic location, a proactive stochastic bi-level model is developed, where the upper level assigns to minimize expected operation cost of the technical VPP and the lower level associates with maximizing the private owner's profits. Note that, the uncertainty set encompasses the real-time energy market price as well as renewables power production. Afterward, the proposed bi-level model is converted to a mixed-integer linear programming problem through Karush–Kuhn–Tucker optimality conditions along with strong duality theory. Moreover, the impacts of implementation of three different pricing schemes including fix pricing, time of use, and real-time pricing on the market trading strategy of VPP and also its interactions with private owners is investigated through comparative case studies. The obtained results demonstrate that the proposed approach could allow both VPP aggregator and private owners to take the advantages of the market conditions.

Optimal multi-market scheduling of a sustainable photovoltaic-oriented distribution network hosting hydrogen vehicles and energy storages: A regret assessment optimization

One of the goals of the smart-grids is to increase the penetration of renewable energy sources like photovoltaics (PVs) and hydrogen-based resources in a controllable manner. To fully utilize these resources in smart cities, dealing with a significant amount of generation and demand uncertainty may be necessary. This article proposed a two-stage optimization procedure smart distribution grid at the city level with a high penetration of solar and hydrogen-powered cars considering the real-time and day-ahead markets. In addition to renewable resources, the proposed network also considers energy storage (ESs), generator units (GUs), and demand response programs (DRPs). In this paper, uncertainties involved in the system are modeled using a scenario-based method. Then, a regression index, so-called maximum relative regret (MRR) derived from the stochastic p-robust optimization (SPRO) method, is utilized to model the imposed regret on uncertainties. Uncertainties in the proposed system include PV generation, electricity and hydrogen demands, real-time market prices, and hydrogen-powered car behavior. The IEEE 33 bus test system has chosen to evaluate the proposed regret-based framework. The findings show that the proposed approach is well-suited to the conservative SPRO technique for the employed system, in which the projected cost of the objective function increases by just 3.40 % for a 45.42 % decrease in the MRR.

Risk constraint techno-economic assessment of photovoltaic-penetrated distribution network considering hydrogen vehicles in a multi-market environment

Challenges arising from increasing the penetration roof-top photovoltaic (PV) system are mostly related to the uncertainties concerned with PV generation units, which significantly impact the optimal operation of the power grid and may impose economic risks on the network operator. This article analyzes the real-time and day-ahead financial risk of a PV penetrated smart distribution grid with hydrogen-powered vehicles demands. The proposed approach models the uncertainties using a scenario-based method and evaluates the resultant risk of these uncertainties by utilizing the downside risk constraint (DSRC) approach. Compared to other risk-measures, DSRC method is a convex non-equilibrium approach and provides a closed-loop solution which is incorporated explicitly as a constraint in the problem. Besides, due to the linearity of the proposed DSRC method, there is no increased computational burden of the final risk-based model. The uncertainties involved in the model include PV generation, electrical demand, real-time market price, and behavior of hydrogen-powered vehicles. The studied test system is the standard IEEE 33 bus distribution network which 14 buses have installed PV with capacity of 50-150% of their peak load. The results validate a 100% reduction of expected downside risk (EDR), while the total cost is increased only by 3.8%.

Exploring the profitability of using electric bus fleets for transport and power grid services

Electric buses (E-buses) are increasingly replacing internal combustion powered bus fleets. They can further function as distributed energy storage units. We investigate the feasibility of the novel “Bus-to-Grid” (B2G) or “E-Buses as Power Storage” (EBaPS) concept, which allows battery E-buses to provide transportation as well as power-grid services. By aggregating the electrified bus fleets into Virtual Power Plants and discharging the energy to the grid, a bus operator can provide grid services and open up a secondary revenue source in addition to fareboxes. We discuss two contracts the bus operator can arrange with the grid. The first one is “Sale by Market Price (SbMP)”, presuming that the buses can discharge and sell the energy at real-time market prices. The second one is “Frequency Control Reserve (FCR)”, in which the buses discharge on immediate requests of the grid. We formulate the two contracts and their associated fleet allocation problems as two non-linear optimization problems maximizing the operator’s profit. Optimal bus charging, discharging, and dispatching plans are solved on an aggregate level for strategic planning, allowing scalability to large fleet sizes. The profitability of the contracts is illustrated based on the schedule and demand of a real bus line and observed time-of-day dependent price variations of the energy market.

Computational intelligence based PEVs aggregator scheduling with support for photovoltaic power penetrated distribution grid under snow conditions

• A snow loss-aware deep learning-based short-term PV yield predictor is developed. • Stochastic day-ahead scheduling of an independent PEVs aggregator is investigated. • A novel local balancing service offered by the PEVs aggregator to DSO is proposed. • The service benefits PEV owners through providing extra energy demand of the grid. This paper addresses the issue of optimal day-ahead scheduling of a plug-in electric vehicles (PEVs) aggregator that participates in the electricity market and offers an out-of-market balancing service to the local renewable power penetrated distribution system in a snow-prone area. The proposed balancing service provides a reliable source of flexibility for the extra real-time energy demand of the distribution system operator (DSO) which originates from the difference between its day-ahead bids and the actual demand. The problem is investigated on a snowy day when the DSO's day-ahead decisions encounter more uncertainty due to the considerable effect of snow loss on the DSO's photovoltaic plant performance. The aggregator's scheduling is formulated as two-stage stochastic programming which minimizes the PEVs’ charging cost. Monte Carlo simulation and K-means clustering are implemented to generate scenarios of driving patterns and real-time energy market prices, respectively. Offering the balancing service requires day-ahead predictions of the photovoltaic power and the grid load demand which are modeled using long short-term memory networks. The problem is formulated as mixed-integer linear programming. The results show that the proposed scheduling approach reduces the PEVs’ charging cost by 53% and guarantees the grid normal operation. Moreover, the balancing service can reduce the expected PEVs’ charging cost and the DSO's real-time cost by 12% and 14%, respectively. © 2017 Elsevier Inc. All rights reserved.

A Study on Farmers’ Perception about Uzhavan App in Select Districts of Tamil Nadu

ICT provides more facilities to farmers by introducing agricultural mobile apps and encourage the farmers for effective farming. These mobile apps provide agriculture related information to farmers in timely and cost effective manner. The present study is carried out to find the farmers perception on uzhavan app which is developed by Government of Tamil Nadu. More than 5 lakh farmers have registered in this app and get various benefits. The uzhavan app has been used by many farmers in gaining more information about agricultural activities. Respondents revealed that dissemination of information through the uzhavan app is valuable and user friendly. The major challenges faced by the farmers were network problem mainly during the ATMA training registration, lack of price details of few commodity and in minor problem time consumes to use all services, app is quite difficult to use due to poor content visibility. From the study, it has been found that majority of farmers had good perception about the uzhavan app and they are satisfied with the features that support them in farming and they also expect some other features need to be integrated in uzhavan mobile app. The uzhavan app provides real time market price of some agricultural commodities and it has greater impact in selling farmers produce at fair price in mandis. Meanwhile, the features of e market online platform helps farmers to connects directly with traders and sell their produce at good price. The availability of farm guide in app provides information regarding fertilizer recommendations practices where most of sample respondents adopted recommended usage level and has positive impact in farming by increasing the yield of farmers.

Modeling the risk-based decisions of the microgrid in day-ahead energy and reserve markets considering stochastic dispatching of electrical and thermal energy storages

• The MGO’s bids in the markets are optimized with dispatching of local resources. • Uncertainties of electrical demand and power generation of PV are modeled. • A robust two-stage stochastic optimization model is developed. • The most capacity of electrical energy storage is used to provide reserve capacity. • With increasing the robust parameter, the MGO’s cost increases. In this paper, the electrical and thermal energy management problem of a micro-grid operator (MGO) is addressed under uncertainties aiming at participating in the day-ahead energy and reserve markets. For this purpose, a robust two-stage stochastic model is developed to protect the first stage MGO’s decisions, i.e., its bids in the energy and reserve markets, against the uncertainty of the real-time energy market price. This is done through stochastic dispatching of the MG resources which includes the electrical and thermal energy storages and the combined heat and power unit as the second-stage decisions. The results showed that the MGO’s expected total cost decreases when it participates in both the energy market and the reserve market in comparison with the case it only participates in the energy market. Also, the risk-based behavior of the MGO showed that increasing the robust parameter decreases the reserve provided for the market and the net power trading with the market. However, the proposed robust two-stage stochastic model leads to a smaller reduction of the MGO’s first-stage decisions in the worst case in comparison with the conventional methods, i.e. deterministic and probabilistic ones. This issue proves the effectiveness of the proposed approach to protect the MGO’s decisions against the uncertainties.

Optimal Scheduling of a Self-Healing Building Using Hybrid Stochastic-Robust Optimization Approach

This article provides a two-stage robust energy management method for a self-healing smart building that can handle contingencies that occur during real-time operation. Aside from an electrical link with the distribution network, the smart building is equipped with a diesel generator and photovoltaic solar power generating systems. The energy management system should be smart enough to plan different resources based on the situation. At first, bilevel programming identifies critical faults for affected components based on mean time to repair. After identifying major failures, the faults are described in operational scenarios, and a two-stage hybrid robust-stochastic programming technique is used to determine the bid/offer in day-ahead and real-time energy markets, in which stochastic programming is responsible for considering the uncertainty of faults, and the robust optimization approach is used to cope with the uncertainty of real-time market prices. After linearization, the final optimization is modeled as mixed-integer linear programming in the GAMS optimization package. For the studied smart building, the daily operational cost is expected to increase from $25.794 (for the deterministic case) to $ 28.097 (for the most conservative case) due to the uncertainty of real-time market prices. Due to power shortages caused by the failure of components, the total expected not-supplied load is 6.72 kW (2.53%). A comparison between naive and self-healing scheduling indicated that naive energy management will charge an additional $ 2.75 without considering the probability of components failures under the deterministic case.

Energy trading efficiency in ERCOT’s day-ahead and real-time electricity markets

The efficient market hypothesis (EMH) is a fundamental tenet of active and unfettered market trading. The EMH for intertemporal trading of a commodity such as natural gas implies that today’s futures price is an unbiased predictor of a future delivery period’s spot price and that the difference between today’s futures and spot prices reflects the commodity’s cost of carrying. The EMH for inter-regional trading requires the commodity’s regional price difference to equal the inter-regional transportation cost. Using regional hourly data for January 1, 2011 to December 31, 2020 from the Electric Reliability Council of Texas (ERCOT), we investigate whether the EMH is empirically valid in all ERCOT’s wholesale electricity markets and, if not, the extent of ERCOT’s energy trading inefficiency and what can be done to reduce it. By estimating a parsimonious system of eight price-level regressions and four price-difference regressions, we reject the interday EMH for all regions and the inter-regional EMH for all regional market pairs. However, ERCOT’s extent of trading inefficiency is mild when compared to the wholesale energy prices. Our empirical examples have two policy implications: enhancing ERCOT’s interday trading efficiency entails improving the day-ahead forecasts for solar and wind generation and refining ERCOT’s indicative real-time market prices, while enhancing ERCOT’s inter-regional trading efficiency requires transmission capacity expansion to reduce transmission congestion and line losses.

Optimal risk-constrained stochastic scheduling of microgrids with hydrogen vehicles in real-time and day-ahead markets

By growing interest in hydrogen vehicles (HVs), hydrogen fueling stations (HFSs) that convert electric power to hydrogen to supply HVs have emerged as a new asset for power grids. To safely and consistently supply HFSs with power, the use of microgrids (MGs), including various flexible generation units, is considered to be a reliable choice. This paper proposes a competence MG scheduling model. In this model, an optimal coordination of HFSs with demand response (DR), energy storage systems (ESS), and appropriate multi-market mechanisms is addressed. Also, a reformulated version of a risk-constrained stochastic scheduling (RSS) model is used to minimize the MG operation cost. The uncertainties associated with the real-time market price, renewables, electrical loads, and HVs are handled by considering conservativeness parameters. In this model, linearized AC optimal power flow (ACOPF) equations are included in the mixed-integer linear programming (MILP) problem to satisfy the security of MG operation. The proposed model is examined on a 21-bus MG while considering various case studies. The results show that the operation of HFSs provides a profit of 254.5$/day by selling hydrogen for MG operator. In addition, it is found that developing the HFS technology can reduce the total daily operation costs of MG by up to 9.6%. It is also shown that participation of MG in both day-ahead and real-time markets leads to a reduction of 11.7% in the operating costs. Moreover, we show that employing DR programs leads to operation cost reduction and load flattening during high-demand hours. The security constraints keep the voltage between 0.97 p.u. and 1 p.u and the loss of lines within a reasonable range. Finally, a comprehensive comparison between our perceptive RSS with traditional stochastic scheduling and conservative RSS is carried out to show the effectiveness of the proposed method.

Optimal Bidding Strategy for Physical Market Participants With Virtual Bidding Capability in Day-Ahead Electricity Markets

Virtual bidding provides a mechanism for financial players to participate in wholesale day-ahead (DA) electricity markets. The price difference between DA and real-time (RT) markets creates financial arbitrage opportunities for financial players. Physical market participants (MP), referred to as participants with physical assets in this paper, can also take advantage of virtual bidding but in a different way, which is to further amplify the value of their physical assets. Therefore, this work proposes a model for such physical MPs to maximize the profits. This model employs a bi-level optimization approach, where the upper-level subproblem maximizes the total profit from both physical generations and virtual transactions while the lower-level model mimics the multi-period network-constrained DA market clearing process. In this model, uncertainties associated with other MPs as well as RT market prices are considered. Moreover, the conditional value-at-risk (CVaR) metric is utilized to measure the risk of diverse strategies. The optimal strategy of the strategic physical MP is derived by solving this bi-level optimization model. The proposed bi-level model is transformed to a single level mixed integer linear programming (MILP) model using Karush-Kuhn-Tucker (KKT) optimality conditions and the duality theory. Case studies show the effectiveness of the proposed method and reveal physical MPs may choose to deploy virtual transactions in a very different way than pure financial MPs.

Developing Robust Bidding Strategy for Virtual Bidders in Day-Ahead Electricity Markets

Purely financial players without any physical assets can participate in day-ahead electricity markets as virtual bidders. They can arbitrage the price difference between day-ahead (DA) and real-time (RT) markets to maximize profits. Virtual bidders encounter various monetary risks and uncertainties in their decision-making due to the high volatility of the price difference. Therefore, this paper proposes a max-min two-level optimization model to derive the optimal bidding strategy of virtual bidders. In this model, the risks of uncertainties associated with the rivals’ strategies and RT market prices are managed by robust optimization. The proposed max-min two-level model is turned into a single-level mixed integer linear programming model through duality theory (DT), strong duality theory (SDT), and Karush-Kuhn-Tucker (KKT) conditions. An illustrative case is designed to demonstrate the advantages of the proposed model over the deterministic model. Moreover, case studies on the IEEE 24-bus test system validate the applicability of the proposed model.

A novel hybrid two-stage framework for flexible bidding strategy of reconfigurable micro-grid in day-ahead and real-time markets

Microgrids are going to be used in future intelligent grids as a promising technology to enable widespread utilization of renewable energy sources in a highly efficient and reliable manner. It is known that reconfiguration of micro-grids, using tie-line and sectionalizing switches, can provide more operational flexibility. Additionally, coordinated scheduling of flexible loads and energy storage systems can play an important role in the optimal scheduling of micro-grids; thus lowering the costs. This paper proposes an optimal bidding strategy for a micro-grid in day-ahead and real-time markets, based on AC power flow model, considering the hourly reconfiguration of the micro-grid. Fuel cell-based hydrogen energy storage and multiple shiftable loads are considered in the proposed method according to the load’s activity schedule. A reconfigurable micro-grid incorporates energy production and consumption of its local components to trade power in both day-ahead and real-time markets in order to maximize its profit as a private entity. The bidding problem faces issues due to the high level of uncertainties, consisting of wind power generation and electric load as well as variations of market prices. A hybrid two-stage bi-level optimization model is proposed to manage such uncertainties so that wind power, load demand, and day-ahead market prices are handled through scenario-based stochastic programming, and an information gap decision theory is applied to model the uncertainty of real-time market prices under two strategies, namely risk-seeker and risk-averse. The numerical simulation results confirm the effectiveness of the proposed model.