Real-time Electricity Market Research Articles

A new bi-level model for the false data injection attack on real-time electricity market considering uncertainties

State Estimation (SE) plays a critical role in various applications of power systems including the electricity market. False Data Injection Attacks (FDIAs) are capable of manipulating the power system SE process aiming to gain financial profits by the players. In this paper, the potential economic influences of FDIA on SE and consequently the Real-Time (RT) electricity market are examined. Therefore, a new bi-level optimization framework is proposed to realistically model the FDIAs from the attacker perspective. At the upper-level, the attacker intends to intelligently choose the most critical congested transmission lines and manipulate their associated meter readings to maximize the expected financial profitability. At the lower-level, the RT market-clearing model is formulated under the influence of FDIA. Considering the incomplete attacker's prior knowledge of the network topology i.e., connection/disconnection of transmission lines, the angles of the phase-shifting transformers, and the real-time load uncertainty, the proposed formulation is extended to a new bi-level scenario-driven stochastic model. The developed attack model, which must remain undetectable by the system operator in all scenarios, is solved using the Karush–Kuhn–Tucker (KKT) approach. The correctness and effectiveness of the proposed optimization scheme have been validated in GAMS software using CONOPT and OQNLP solvers based on the IEEE 14-bus test system and also comparative results are presented to demonstrate the merits of the proposed formulation.

Joint contribution of RTEM and AGC system for frequency stabilisation in renewable energy integrated power system

AbstractIncreasing penetration of variable renewable generations will diminish system inertia thereby degrading the conventional frequency regulation capability. As a result, maintaining frequency stability will be more and more challenging with traditional approaches. Even though renewable sources integration would jeopardise the grid stability, it also presents several opportunities as well. For example, converter‐interfaced generators can bid in real‐time electricity markets (RTEM) and provide short‐time dispatch to minimise load‐generation mismatch. In this paper, an integrated approach that accommodates discrete automatic generation control (AGC) system with a regulation mileage framework and RTEM model to balance generation and consumption is proposed. The RTEM model is assumed to have a five‐minute dispatch trading interval which is to some extent comparable to the discrete AGC system. Furthermore, a fractional order PID (FOPID) controller is equipped in the AGC system whose parameters are tuned using a novel metaheuristic‐based optimisation called Lichtenberg Algorithm (LA). The proposed framework is tested in a three‐area system under several operating conditions to reveal the improvement in the dynamic performance of the system. The objective function is also incorporated with mileage payment that allows a fair compensation rule for all the units.

Risk-based optimization for facilitating the leasing services of shared energy storage among renewable energy stations

Due to the inherent power output correlation and uncertainty, renewable energy stations normally incur the deviation penalty in the day-ahead and real-time electricity market. Meanwhile, shared energy storage operators have been appearing to provide energy storage leasing services for neighboring renewable energy stations. In this context, this paper presents a novel optimization strategy to provide leasing services for renewable energy station clusters while improving the utilization rate and revenue of shared energy storage simultaneously. Especially, the proposed strategy utilizes a two-stage optimization model to incorporate the overselling risk. In the first stage, a matching index is defined to select a cluster of wind and solar power stations in the geographically-close region, when a set of highly complementary stations are selected by matching the typical output curve of the shared energy storage. In the second stage, an optimization strategy is determined to explore the benefit and risk of overselling for shared energy storage with the goal of maximizing the total revenue, when the correlation of wind and solar power output is realized in the scenario generation and sampling process. The results of numerical experiments have demonstrated that employing a moderate overselling method can provide an economical and efficient operational solution to improving the utilization of shared energy storage.

A two-level optimization framework for battery energy storage systems to enhance economics and minimize long-term capacity fading

This paper proposes a two-level optimization framework for a battery energy storage system to maximize revenue with consideration of the phenomena that cause battery's capacity fading. Instead of solving the scheduling problem as a singular problem, a two-level optimization framework is introduced. The upper-level optimization focuses on maximizing revenue by arbitrage in a real-time electricity market. With the determined operating schedule, the lower-level optimization, solved with particle swarm optimization, determines the optimal charging current that mitigates the side reactions while maintaining economic performance. With the optimal charging current and operating schedule, a pseudo-2D electrochemical model is used to simulate the battery behavior. To reflect the actual behavior of the lithium-ion battery, two side reactions, solid electrolyte interface formation and Lithium plating, are simulated. A case study using California energy prices is presented. With the proposed framework, the results show that the battery's lifespan is extended by up to 5.1 % and overall revenue increases by up to 9.8 % compared to a singular economic optimization utilizing the standard constant current, constant voltage charging protocol.

System Profit Improvement of a Thermal–Wind–CAES Hybrid System Considering Imbalance Cost in the Electricity Market

Studying a renewable energy integrated power system’s features is essential, especially for deregulated systems. The unpredictability of renewable sources is the main barrier to integrating renewable energy-producing units with the current electrical grid. Due to its unpredictable nature, integrating wind power into an existing power system requires significant consideration. In a deregulated electricity market, this paper examines the implications of wind farm (WF) integration with CAES on electric losses, voltage profile, generation costs, and system economics. Comparative research was done to determine the impact of wind farm integration on regulated and deregulated environments. Four randomly chosen locations in India were chosen for this investigation, together with real-time information on each location’s real wind speed (RWS) and predicted wind speed (PWS). Surplus charge rates and deficit charge rates were created to assess the imbalance cost arising from the discrepancy between predicted and real wind speeds to calculate the system economics. When the effect of imbalance cost is considered, the daily system profit shows a variation of about 1.9% for the locations under study. Customers are always seeking electricity that is dependable, affordable, and efficient due to the reorganization of the power system. As a result, the system security limit could be exceeded or the system might function dangerously. The final section of this paper presents an economic risk analysis using heuristic algorithms such as sequential quadratic programming (SQP), artificial bee colony algorithms (ABC), and moth flame optimization algorithms (MFO). It also discusses how the CAES is used to correct the deviation of WF integration in the real-time electricity market. Economic risk analysis tools include value-at-risk (VaR) and conditional value-at-risk (CVaR). The entire piece of work was validated using a modified IEEE 30-bus test system. This works shows that with a three-fold increase in wind generation, the risk coefficient values improves by 1%.

A Transactive Energy Framework for Inverter-Based HVAC Loads in a Real-Time Local Electricity Market Considering Distributed Energy Resources

Rapidly increasing distributed energy resources (DERs) bring more fluctuating output power to the distribution network and put forward a higher requirement on local regulation resources for maintaining the network's balance. Heating, ventilation, and air conditioning (HVAC) loads account for more than 40% of power consumption in modern cities and have huge regulation potential as flexible loads. However, HVACs equipped with inverter devices have rarely been studied for providing regulation services in the local electricity market (LEM), even though they have exceeded regular fixed-speed HVACs. To address this issue, this article proposes a real-time LEM and a distribution network's optimization framework to exploit the regulation potential of inverter-based HVACs considering multiple DERs. This LEM can avoid iterations in real time and significantly decrease the difficulty related to the participation of small end-users in urban distribution networks. Moreover, in this article, we propose a transactive capacity evaluation method to assist end-users in deciding their inverter-based HVACs regulation capacities in the real-time LEM, which considers buildings’ thermal features, users’ multiple comfort requirements, and dynamic ambient temperature. On this basis, a multilevel bidding strategy is developed for inverter-based HVACs to decrease energy cost, increase fluctuating DERs local utilization rate, and alleviate the distribution network's congestion. Finally, a realistic distribution network is utilized to verify the effectiveness of the proposed methods.

Procurement of Energy, Primary Regulation, and Secondary Regulation Reserves in Battery Energy Storage Systems Integrated Real-Time Electricity Markets

In recent years, battery energy storage systems (BESS) have been considered as promising resources to provide regulation services because of their operational flexibility. In this article, a novel framework and mathematical model are proposed for simultaneously procuring primary regulation (PR) and secondary regulation (SR) reserves alongside energy, in a BESS integrated, locational marginal price (LMP) based real-time market. The BESS submits discharge offers considering degradation cost, based on depth of discharge and discharge rate. The proposed model takes into consideration <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> cleared day-ahead market (DAM) schedules and accounts for scenarios of load deviations and contingencies in real-time operations. The impact of BESS participation on system operation and market settlement is examined considering two participation options: 1) BESS bidding as the generator/load with price quantity pair; 2) BESS participation using the offers based on degradation cost. The proposed model is validated on the IEEE Reliability Test System to demonstrate its functionalities.

Economic Enhancement of Wind–Thermal–Hydro System Considering Imbalance Cost in Deregulated Power Market

Studying the property of the combination of renewable energy sources in the existing power systems is of great importance, and especially in the case of deregulated systems. The uncertainty of renewable sources is the largest barrier to integrating renewable-energy-producing units into the existing electrical infrastructure. Due to its uncertainty, integrating wind power into an existing power system requires extra consideration. In this work, the impacts of wind farm (WF) integration and a pumped hydroelectric storage system (PHES) on the electric losses, voltage profiles, generation costs, and system economy in a deregulated power market were studied. A comparative study was performed to determine the impact of wind farm integration on regulated and deregulated environments. Four locations in India were chosen at random for this work, and we used the real-time statistics for the actual wind speeds (AWSs) and forecasted wind speeds (FWSs) for each chosen location. To determine the system economy, surplus charge rates and deficit charge rates were developed to evaluate the imbalance cost resulting from the mismatch between the predicted and actual wind speeds. Considering the effect of the imbalance cost, the system profit/day varies by an average of 1.6% for the locations studied. Because of the reorganization of the power system, consumers constantly look for reliable and affordable power that is also efficient. As a result, the system security limit may be breached, or the system may run in a dangerous state. Lastly, in this paper, an economic risk analysis is presented with the help of heuristic algorithms (i.e., artificial bee colony algorithm (ABC) and moth–flame optimization algorithm (MFO)), along with sequential quadratic programming (SQP), and the way in which the PHES is used to compensate for the deviation in the WF integration in the real-time electricity market is also presented. The value at risk (VaR) and conditional value at risk (CVaR) were used as the economic risk analysis tools. According to the work, with the increase in the wind generation, the system risk improves. The results show that, as the wind generation increases by three times, there is an improvement in the risk coefficient values by 1%. A modified IEEE 14-bus test system was used for the validation of the entire work.

Non-cooperative differential game and feedback Nash equilibrium analysis for real-time electricity markets

Continuous liberalization of electricity markets makes a strong correlation between the economic behaviors of market participants and their profits. To guide the production of generators before the market reaches equilibrium, a new framework is proposed to model the real-time electricity market with help of optimal control theory. The market price is described as the dynamic state using a sticky price model. We establish an N-person non-cooperative differential game model, where all participants try to maximize their profits independently only by observing power prices. The existence of feedback Nash equilibrium and uniqueness of optimal price trajectory are proved in detail, which will help other scholars build an effective differential game model. To protect the privacy of all generators, a distributed algorithm is proposed based on neurodynamic and consensus theory, which only requires information exchange among neighboring participants. Furthermore, a special case of a duopoly power market is investigated in detail and we provide a feedback time-continuous solution for each generator. Compared with the commercial Cplex solver, the proposed distributed algorithm performs better in convergence speed.

Efficient energy management of wireless charging roads with energy storage for coupled transportation–power systems

Wireless charging roads equipped with energy storage systems are promising electric vehicle charging solutions by virtue of their strong advantages in time saving and reduced pressure on the existing power infrastructure. Integration of wireless charging roads into the existing electricity market and efficient management of the corresponding energy storage system are crucial for successful implementation of the wireless charging road systems. In this work, we develop a coupled transportation–power system framework for incorporation of a wireless charging road system into the real-time electricity market. In addition, we propose a Lyapunov optimization-based control strategy to manage the energy storage system in a cost-efficient manner. Our simulation study demonstrates that efficient control of the energy storage system not only reduces the energy costs of the entire wireless charging road system but also alleviates the pressure produced by the wireless charging load on the existing power grid. In our two numerical examples, the energy costs are reduced by 2.61% and 15.34%, respectively. Two indicators of power grid pressure: time average of maximum and time average of standard deviation of locational marginal prices, are reduced by 10.65% and 69.33% for the first numerical example and 5.11% and 34.73% for the second numerical example.

Day-ahead risk averse market clearing considering demand response with data-driven load uncertainty representation: A Singapore electricity market study

Demand response program is being implemented in the National Electricity Market of Singapore, which boosts the flexibility of demand side to actively participate in the real-time electricity market. Meanwhile, it is also significant to implement such a program in the day-ahead market, since generation companies could arrange their generating plans and load providers are able to adjust their hourly purchasing schedules. However, uncertain factors should be considered in the demand response program of the day-ahead market, such as the uncertain electricity load. Regarding the issue, this paper proposes a day-ahead bidding and clearing framework considering demand response with uncertain and correlated nature of electricity loads. To this end, a data-driven Dirichlet process mixture model is introduced to represent the load uncertainty, which might bring about the economic risk. To further reduce such a risk, a worst-case conditional value at risk is integrated into our proposed framework, and a WCVaR based two-step risk averse market clearing model is proposed. Finally, we conduct numerical studies based on the Singapore electricity market. Numerical studies demonstrate the outperformance of Dirichlet process mixture model for the load uncertain representation, and also verify that the worst-case conditional value at risk based market clearing model could effectively reduce the economic risk while maximizing the social welfare.

HVAC system dynamic management in communities via an aggregation–disaggregation framework

The aggregation and disaggregation problem of Heating Ventilation and Air Conditioning (HVAC) systems in the building at the community-level have been considered in this work. The aim is to provide capacity reserves as a Virtual Power Plant (VPP) to support power system operation, improving grid reliability and efficiency in the face of increased use of renewable energy. Motivated by the layered operating structure of the power grid, an aggregation–disaggregation framework which operates at both slow and fast time-scales is proposed. At the slow time-scale, each building forecasts its HVAC system future electric power consumption information using predictions of disturbance and information of thermal zone via solving certain optimization problems. These values will be collected by a control center such as a community aggregator. Acting as a VPP, the control center determines each HVAC system future nominal electric power consumption by participating in the real-time electricity market or an independent power allocation plan. At the fast time-scale, a modified primal–dual gradient method based distributed control scheme is designed such that under real disturbances, the system can track the nominal consumption as closely as possible using only neighboring communications and local measurements. Finally, a numerical study demonstrates the effectiveness of the proposed framework.

QCAE: A quadruple branch CNN autoencoder for real-time electricity price forecasting

Real-time electricity market data is highly volatile and very noisy. The properties of such data make forecasting models difficult to develop, with traditional statistical models in particular affected by the “curse of dimensionality” for such data. However, autoencoders, or neural networks specifically designed to reduce the noise and dimensions of input data, may prove useful to advance the accuracy of real-time price forecasting models. This paper studies the optimal design of such an autoencoder, developing a quadruple branch, CNN-based autoencoder (QCAE) which is pre-trained and then directly linked to a forecasting model. The QCAE compresses the input data in both time and feature directions. Ablation analyses verify the architecture of the QCAE, and its integration with the forecasting model is tested and validated on fifty generators in the New York Independent System Operator (NYISO) power grid. The QCAE forecasting framework outperforms benchmark and state-of-the-art models with an average improvement of 6.3% in sMAPE and 3.10% in MAE.

Competitive Bidding Strategies for Online Linear Optimization with Inventory Management Constraints

This paper develops competitive bidding strategies for an online linear optimization problem with inventory management constraints in both cost minimization and profit maximization settings. In the minimization problem, a decision maker should satisfy its time-varying demand by either purchasing units of an asset from the market or producing them from a local inventory with limited capacity. In the maximization problem, a decision maker has a time-varying supply of an asset that may be sold to the market or stored in the inventory to be sold later. In both settings, the market price is unknown in each timeslot and the decision maker can submit a finite number of bids to buy/sell the asset. Once all bids have been submitted, the market price clears and the amount bought/sold is determined based on the clearing price and submitted bids. From this setup, the decision maker must minimize/maximize their cost/profit in the market, while also devising a bidding strategy in the face of an unknown clearing price. We propose DEMBID and SUPBID, two competitive bidding strategies for these online linear optimization problems with inventory management constraints for the minimization and maximization setting respectively. We then analyze the competitive ratios of the proposed algorithms and show that the performance of our algorithms approaches the best possible competitive ratio as the maximum number of bids increases. As a case study, we use energy data traces from Akamai data centers, renewable outputs from NREL, and energy prices from NYISO to show the effectiveness of our bidding strategies in the context of energy storage management for a large energy customer participating in a real-time electricity market.

Shared-constraint approach for multi-leader multi-follower game of generation companies participating in electricity markets with carbon emission trading mechanism

There is a high degree of overlap between market entities covered by the carbon emission trading (CET) market and electricity market, and the two markets are closely related. Therefore, it is of great significance to analyze the impact of the CET market mechanism on the bidding strategy of generation companies (GENCOs) and electricity market clearing results. In this paper, the problem of GENCOs participating in both the day-ahead and real-time balancing electricity market within the CET mechanism is studied as a multi-leader multi-follower (MFML) Stackelberg game problem. The GENCOs are considered as leaders in the game who decide on the submitted bid and offer in terms of price and quantity to maximize their expected profit in the market, while both the clearing problems of day-ahead and real-time markets are considered as followers on the lower level. In order to expand the Nash equilibrium of the considered MFML game, the shared-constraint approach is applied to modify this problem. Two typical Nash equilibria of the proposed MLMF game are analyzed and discussed. Finally, the results of the simulations conducted on a modified IEEE 39-bus system and a real 2778-bus system indicate that GENCOs tend to submit bids with a higher price in the market under the global Nash equilibrium solution. Within the CET mechanism, the overall market clearing share of the gas-fired units increases from 21.76% to 18.1%–32.33% and 19.51%, at valley and peak loads, respectively. Thus, the operation of the CET market will make low-emission units more competitive in the electricity market.

Stealthy and profitable data injection attack on real time electricity market with network model uncertainties

The economic operations of real time (RT) electricity markets are vulnerable to false data injection (FDI) attacks, designed by cyber-attackers. Strategically, the RT locational marginal prices (LMPs) are stealthily altered by manipulating some of measurement data and it provides conditions for profitable financial misconduct in the electricity market. This paper proposes a new Monte Carlo-based FDI attack strategy for a cyber-attacker, who has very limited knowledge about the topology and parametric information of targeted network, which called an attacker with model topology-parametric uncertainties (TPUs). The main feature of the proposed attack is that despite the model errors, the attacker can guarantee the stealthy and profitable attack in advance, since the attack is designed based on an optimization problem of worst-case robust against uncertainties. Two 5-bus PJM and 30 IEEE bus systems are used to demonstrate the success of such cyber-attacks in real-time electricity markets.

Research on bidding strategy of virtual power plant considering carbon-electricity integrated market mechanism

Distributed energy resources and controllable load can be aggregated in virtual power plant to participate in the bidding of day-ahead electricity market, intraday demand response market, regulation market, real-time electricity market and carbon trading market. However, the uncertainty output of distributed energy generation brings higher transaction risk to virtual power plant. Given this background, an operation model including wind turbine, electric vehicle, gas turbine and controllable load are constructed in virtual power plant. Through further analysis the carbon-electricity integrated market characteristics, a carbon-electricity integrated optimal bidding strategy of the virtual power plant is constructed, and the uncertainty of renewable energy power generation, load and market price is considered. Some assumptions are made, and a joint bidding strategy model for a VPP participating in bidding of the carbon-electricity integrated market is presented based on the robust optimization theory. Then, the commercial solver CPLEX 12.2 is next used to solve the developed robust optimization model. Finally, a virtual power plant demonstration area is used as the examples to demonstrate the feasibility and efficiency of the developed model and algorithm. The result shows that the electric vehicles and demand response transaction can effectively stabilize output fluctuation of renewable energy power generation, promote the utilization of renewable energy and improve the economic benefits of virtual power plant. Introducing carbon trading mechanism can significantly reduce the bidding output of high-carbon generation unit, and then affect the energy output structure and bidding strategy in virtual power plant.

Wind power bidding coordinated with energy storage system operation in real-time electricity market: A maximum entropy deep reinforcement learning approach

The wind power forecasting error restricts the benefit of the wind farm in the electricity market. Considering the cooperation of wind power bidding and energy storage system (ESS) operation with uncertainty, this paper proposes a coordinated bidding/operation model for the wind farm to improve its benefits in the electricity market. The maximum entropy based deep reinforcement learning (RL) algorithm, Soft Actor-Critic (SAC) is used to construct the model. The maximum entropy framework enables the designed agent to explore various optimal possibilities, which means the learned coordinated bidding/operation strategy is more stable considering the forecasting error. Particularly, penalty terms are introduced into the benefit function to relax the constraints and improve the convergency. The case study illustrates that the learned policy can effectively improve the wind farm benefit while ensuring robustness.

Social Welfare Maximization of Competitive Congested Power Market Considering Wind Farm and Pumped Hydroelectric Storage System

The utilization of wind energy sources with energy storage systems has been increased in the power sector to satisfy the consumer’s energy demand with minimum price. This paper presents the impact of a wind farm (WF) and pumped hydroelectric storage (PHS) system in the competitive electricity market under a congested transmission system. The PHS system is used to compensate for the deviation of WF generation in the real-time electricity market. To investigate the impact of the proposed method, initially, the market-clearing power problem is solved without consideration of WF and PHS systems, and again it is solved with the WF and PHS systems. The optimal location of the WF and PHS systems is decided by the bus sensitivity factor (BSF) of these systems. The analysis is carried out by using generator sensitivity factor (GSF) with the help of the moth flame optimization (MFO) algorithm and thereby calculating market clearing price (MCP) and market clearing volume (MCV). The MFO algorithm is used here for the first time for solving the congested market-clearing power problem with the integration of WF and PHS systems under deregulated environment. The presented approach shows the improvement of social welfare after the placement of WF and PHS in the congested deregulated system. Modified IEEE 30 bus system is used to solve the market-clearing power problem and results obtained from the MFO algorithm are compared with the firefly algorithm (FA). Three different real-time wind speed data have been considered here to verify the proposed approach with uncertainty and the continuously changing nature of wind flow. It is discovered that social welfare is improved with the quantity addition of wind power, regardless of optimization techniques.

Competitive bidding strategies for online linear optimization with inventory management constraints

This paper develops competitive bidding strategies for an online linear optimization problem with inventory management constraints in both cost minimization and profit maximization settings. In the minimization problem, a decision maker should satisfy its time-varying demand by either purchasing units of an asset from the market or producing them from a local inventory with limited capacity. In the maximization problem, a decision maker has a time-varying supply of an asset that may be sold to the market or stored in the inventory to be sold later. In both settings, the market price is unknown in each timeslot and the decision maker can submit a finite number of bids to buy/sell the asset. Once all bids have been submitted, the market price clears and the amount bought/sold is determined based on the clearing price and submitted bids. From this setup, the decision maker must minimize/maximize their cost/profit in the market, while also devising a bidding strategy in the face of an unknown clearing price. We propose DEMBID and SUPBID, two competitive bidding strategies for these online linear optimization problems with inventory management constraints for the minimization and maximization setting respectively. We then analyze the competitive ratios of the proposed algorithms and show that the performance of our algorithms approaches the best possible competitive ratio as the maximum number of bids increases. As a case study, we use energy data traces from Akamai data centers, renewable outputs from NREL, and energy prices from NYISO to show the effectiveness of our bidding strategies in the context of energy storage management for a large energy customer participating in a real-time electricity market.