Independent System Operator Research Articles

The clearing strategy of primary frequency control ancillary services market from the point of view ISO in the presence of synchronous generations and virtual power plants based on responsive loads

Since the increase in penetration of renewable energy sources connected to the system reduces the inertia of power systems, the penetration of these sources leads to increase in the requirements of primary frequency control (PFC) services. Fortunately, with the expansion of network intelligence platforms, responsive loads (RL) can be effectively useful in ancillary services in the near future and can be used like traditional power plants. Since these equipment have a high rate of change of status, if they are visible in the market by aggregating (with virtual power plant (VPP)), they can compete with synchronous generations (SG). Because the response speed of the participants in the market can affect the decision independent system operator (ISO) in determining the winning units, therefore in this article, we have proposed a market framework to create competition between SGs and VPPs in providing ancillary services. In the proposed framework, ISO minimizes the weighted sum of power purchase costs from VPPs and SGs. The proposed weighting coefficients express the response speed of each unit. In fact, the desired objective function is affected by two terms, cost and speed. The presented model has been simulated on a test system including four SGs units and one VPP unit in matrix laboratory (MATLAB) software and checked under five different scenarios. The comparison of the obtained results indicates an increase in the possibility of accepting units with a smaller weighting factor and a higher response speed (the meaning of accepting units are market players, i.e. SGs and VPPs).

Analyzing how the timing and magnitude of electricity consumption drive variations in household electricity-associated emissions on a high-VRE grid

Abstract Electrifying the residential sector is critical for national climate change adaptation and mitigation strategies, but increases in electricity demand could drive-up emissions from the power sector. However, the emissions associated with electricity consumption can vary depending on the timing of the demand, especially on grids with high penetrations of variable renewable energy. In this study, we analyze smart meter data from 2019 for over 100 000 homes in Southern California and use hourly average emissions factors from the California Independent System Operator, a high-solar grid, to analyze household CO2 emissions across spatial, temporal, and demographic variables. We calculate two metrics, the annual household electricity-associated emissions (annual-HEE), and the household average emissions factor (HAEF). These metrics help to identify appropriate strategies to reduce electricity-associated emissions (i.e. reducing demand vs leveraging demand-side flexibility) which requires consideration of the magnitude and timing of demand. We also isolate the portion of emissions caused by AC, a flexible load, to illustrate how a load with significant variation between customers results in a large range of emissions outcomes. We then evaluate the distribution of annual-HEE and HAEF across households and census tracts and use a multi-variable regression analysis to identify the characteristics of users and patterns of consumption that cause disproportionate annual-HEE. We find that in 2019 the top 20% of households, ranked by annual-HEE, were responsible for more emissions than the bottom 60%. We also find the most emissions-intense households have an HAEF that is 1.7 times higher than the least emissions-intense households, and that this spread increases for the AC load. In this analysis, we focus on Southern California, a demographically and climatically diverse region, but as smart meter records become more accessible, the methods and frameworks can be applied to other regions and grids to better understand the emissions associated with residential electricity consumption.

Multi-parametric analysis for mixed integer linear programming: An application to transmission upgrade and congestion management

Upgrading the capacity of existing transmission lines is essential for meeting the growing energy demands, facilitating the integration of renewable energy, and ensuring the security of the transmission system. This study focuses on the selection of lines whose capacities and by how much should be expanded from the perspective of the Independent System Operators (ISOs) to minimize the total system cost. We employ advanced multi-parametric programming and an enhanced branch-and-bound algorithm to address complex mixed-integer linear programming (MILP) problems, considering multi-period time constraints and physical limitations of generators and transmission lines. To characterize the various decisions in transmission expansion, we model the increased capacity of existing lines as parameters within a specified range. This study first relaxes the binary variables to continuous variables and applies the Lagrange method and Karush-Kuhn-Tucker (KKT) conditions to obtain optimal solutions and identify critical regions associated with active and inactive constraints. Moreover, we extend the traditional branch-and-bound (B&B) method by determining the problem’s upper and lower bounds at each node of the B&B decision tree, helping to manage computational challenges in large-scale MILP problems. We compare the difference between the upper and lower bounds to obtain an approximate optimal solution within the decision-makers’ tolerable error range. In addition, the first derivative of the objective function on the parameters of each line is used to inform the selection of lines for easing congestion and maximizing social welfare. Finally, the capacity upgrades are selected by weighing the reductions in system costs against the expense of upgrading line capacities. The findings are supported by numerical simulations and provide transmission-line planners with decision-making guidance.

Profit enhancement and imbalance cost reduction with solar PV-fuel cell-EV hybrid system in a wind-integrated day-ahead power market

Abstract In a wind-integrated deregulated network, the wind farm must intimate the power-generating capacity bids to the market controller at least one day before it begins operations. The wind farm's bid submissions are based on the estimated wind speed (EWS); nevertheless, slight differences between the real wind speed (RWS) and the EWS will result in penalties or incentives imposed by the Independent System Operator (ISO). This occurrence is known as the power market imbalance cost, and it has a direct influence on the system's profitability. To mitigate this effect, solar PV and fuel cell storage technologies are used with the wind farm to enhance system profit by offsetting the negative effects of the imbalance cost. Here, solar PV and fuel cells are used to function in the required time i.e. operate in the charging mode when the RWS is greater than the EWS and in discharging mode when the EWS is greater than the RWS to balance the power supply in the grid as to fulfill the power bidding conditions. Furthermore, the study focuses on minimizing potential system risks, which have been assessed using several risk assessment tools such as Value-at-Risk (VaR) and Cumulative Value-at-Risk (CVaR). The work was conducted using an IEEE 14 bus test system. Initially, the solar PV-fuel cell system supplies power to meet local needs, and the remaining energy is sent to the grid to maximize the system's profit. Electric vehicles (EVs) have also been incorporated to maximize the system economy in more quantities and to reduce the system risk further as compared to the solar PV-fuel cell operation. Three different optimization methods, i.e. AGTO (Artificial Gorilla Troops Optimizer Algorithm), ABC (Artificial Bee Colony Algorithm), and SQP (Sequential Quadratic Programming), were used in a comparative analysis to assess the effectiveness of the proposed approach.

Optimizing the Bidding Data for power industry with elastic demand using hybrid Water Cycle Moth Flame Optimization Algorithm

Strategic Optimal Bidding of the data is a compulsory duty for Independent System Operator (ISO) which is the most complicated task that maximizes the profit of the supplier by handling bidding coefficient strategically. This paper endorses a strategy of optimal bidding coefficient data to improve the profit value by latest optimizing technique named hybrid Water Cycle Moth Flame Optimization Algorithm, which achieves a heuristic search thereby obtaining a global search of a stream using Levy flight movement. This method is applied and tested on an Indian-75 Bus system to test and investigate the new strategy whether receiving best solution of profit in comparison with other conventional techniques explained widely. On adding it evaluates the efficacy of the proposed method on the mentioned system through assessing total profit obtained, revenue, power generation, Market Clearing Price and cost of the individual GENCO. In order to show the Statistical Analysis the Box-plot is done to perform the visual data representation of the proposed and conventional methods.

A game-based power system planning approach considering real options and coordination of all types of participants

With the ongoing advancement of global power marketization, the increasing diversity of stakeholders in the power market and their interactions have a significant impact on power system planning and operation. Traditional methods employ game theory to describe the interactions among market participants. However, these approaches fail to include all relevant participant types and overlook the long-term uncertainty value of planning schemes. Therefore, this paper proposes a game theory-based power system planning approach considering real options (RO) and coordination of all types of participants. First, the dynamic gaming interaction among supply-transmission-demand is studied, and the independent system operator is integrated into the game planning framework based on the overall perspective of power market operation. Moreover, the new power supply, grid planning, and operation of distributed generation (DG) are taken as decision variables based on the RO theory, and a game decision model of all participants in the power system planning considering the investment uncertainty is constructed. Finally, the iterative search algorithm is employed to solve the model, and the framework is validated based on the IEEE 30-bus system. The results indicate that through collaborative planning involving various market participants, Generation Companies and Transmission Companies expand their investments to maximize profits, while large power consumers reduce their investment in DG and instead increase their direct power purchases to lower costs. Moreover, these participants are inclined to pursue projects with higher asset value volatility, which further enhances their profit. By integrating RO theory with game theory, the proposed approach establishes a comprehensive planning framework that effectively addresses investment uncertainty. This integration enables market participants to make more optimal decisions, ultimately leading to an enhancement in the long-term economic performance of the power system.

Flexible operation of nuclear hybrid energy systems for load following and water desalination

Nuclear hybrid energy systems (NHES) have the potential to provide dependable and emission-free electricity to the grid while also increasing the flexibility and reliability of the electrical grid. Molten salt reactor (MSR) technology can provide consistent, carbon-free electricity while also increasing efficiency, security, and sustainability and reducing nuclear waste. This study investigates the integration of Molten Salt Reactors (MSR) and conventional Pressurized Water Reactors (PWR) with desalination technologies: Direct Contact Membrane Distillation (DCMD), Multi-Stage Flash Distillation (MSFD), and Reverse Osmosis (RO). Dynamic first-principles models were developed and tested using real grid data from the New York Independent System Operator. The results demonstrate that nuclear power is capable of flexibly responding to changing grid demand while simultaneously producing clean water, particularly during periods of low electricity demand. The MSR-RO system was found to be the most efficient in electricity generation and water production, and all hybrid systems reduced CO2 emissions by 356,000 to 682,000 tons annually. Economic analysis reveals that nuclear desalination technologies are cost-competitive with conventional systems, especially when paired with RO. These findings confirm the technical feasibility and environmental benefits of nuclear hybrid systems for sustainable electricity and water production.

Model predictive control of a grid-scale Thermal Energy Storage system in RELAP5-3D

This research delves into the integration and control of a Thermal Energy Storage (TES) system with a Small Modular Reactor (SMR), specifically the NuScale VOYGR SMR module in RELAP5-3D. The research methodology centered on modeling the NuScale VOYGR SMR, a light water pressurized water reactor (LWR) with a power output capacity of 77 MWe per module. The reactor and plant details were sourced from NuScale's final safety analysis report and supplemented by information from the NuScale website. The SMR plays a crucial role in energy generation, and to manage and dispatch the produced energy effectively, a robust storage system is essential. The proposed solution to this challenge is the implementation of the TES system. The selected TES for this research is a two-tank system. The study also employed Model Predictive Control (MPC) to optimize the operation of the TES system in conjunction with the SMR. Various simulations, including accident scenarios, were conducted to assess the system's response and performance. The research leveraged real energy demand data from the California Independent System Operator (CAISO) database and scaled it to reflect the power generation of a single SMR. The findings suggest that while integrating a TES system with an SMR can enhance the performance compared to a standalone SMR, certain scenarios might exacerbate the total power mismatch. The study provides insights into the potential of integrating TES systems with nuclear reactors and the challenges and considerations involved.

Fully distributed convex hull pricing based on alternating direction method of multipliers

In certain power markets, due to the non-convex operation characteristics, generators adhering to the decisions of Independent System Operators (ISO) may struggle to recover costs through local marginal energy sales. ISOs impose discriminatory additional payments as incentives for generator compliance. Convex hull pricing is a unified scheme that significantly reduces these supplementary payments. The Lagrangian dual problem of the Unit Commitment (UC) problem is solved within the dual space to determine convex hull prices. To navigate the computational challenges posed by the real large-scale power systems, we propose a methodology for a global convergence distributed solution, which addresses the Lagrangian dual problem. This methodology is based on the Alternating Direction Method of Multipliers (ADMM) algorithm and incorporates convex hull cut planes to enhance computational efficiency. Moreover, a tight and compact UC model is employed to reduce the number of iterations. Numerical results indicate that if the convex hull descriptions of units can be obtained, our algorithm is capable of providing precise convex hull prices and high-quality solutions within a feasible timeframe, while also maintaining the confidentiality of individual subset unit information.

Integrated scheme analysis and thermodynamic performance study of advanced nuclear-driven hydrogen-electricity co-production systems with iodine-sulfur cycle and combined cycle

A promising method for massive clean hydrogen production is the Very High Temperature Reactor (VHTR)-driven Nuclear Hydrogen Production (NHP) system using the Iodine–Sulfur (IS) cycle. Research on integrated scheme and thermodynamic performance of the VHTR-driven NHP system using the IS cycle has, however, received little attention up to this point, particularly when the combined cycle is employed as the power generation cycle. In order to bridge this research gap, this work proposed and studied two distinct VHTR-driven hydrogen-electricity co-production systems with the IS cycle and combined cycle: the independent operating system and the coupled operating system. Thermodynamics was used to model these two systems, and system thermodynamic performance was examined under the baseline operating conditions. A parametric study was further conducted on how two important operating parameters affected system thermodynamic performance. The primary findings indicated that the coupled operating system outperformed the independent operating system in terms of thermodynamic performance. Additionally, both the independent and coupled operating systems could produce hydrogen at the same rate of 289.8 mol/s, with net electrical power outputs of 61.07 MW and 102.7 MW, respectively, under the baseline operating conditions. Furthermore, it was discovered that a rise in the mass flow ratio for both operating systems would result in a notable reduction in system efficiency.

Occupant-driven end use load models for demand response and flexibility service participation of residential grid-interactive buildings

As demand response becomes increasingly used as a tool to support improved grid flexibility, it is important to consider that there are many potential types of energy end uses that may be used to support such flexibility. Residential appliances, often accounting for 30 % or more of residential energy use, are a currently untapped source of demand flexibility, particularly when aggregated together across homes. To date there has been very limited analysis of residential appliances for use as grid-interactive loads. As such, this research uses disaggregated energy end use data for 564 households, to model the electricity demand flexibility potential of the use of residential dishwashers, clothes washers, clothes dryers, ovens, and ranges (oven + stovetop) on both weekdays and weekends. This includes both at the building level, as well as aggregated to the grid level, specifically the Midcontinent Independent System Operator (MISO) region. This study was divided into two parts. Part 1 focuses on determining appliance-level loads, and Part 2, which involves aggregation to the grid. Findings suggest that among the studied appliances, clothes dryers provide the greatest demand reduction potential for most times of the day, followed by dishwashers and clothes washers. The maximum potential reduction for clothes dryers is found to be approximately at 11:00 a.m. and this potential sustains throughout most of the daytime period. When considering the willingness of households to participate, based on a survey of households in the Midwest region, clothes dryers still have the most potential for demand reduction. The availability of appliances for load modulation on weekdays and weekends indicates similar load reduction potential for all appliances. Overall, the results of this study suggest that there is an opportunity for shifting appliance usage to optimize grid efficiency and enhance demand response strategies.

Renewable-battery hybrid power plants in congested electricity markets: Implications for plant configuration

Examining coupled renewable-battery power plants (“hybrids”) in congested areas provides insights into a future of increased wind and solar penetration. Our study focuses on two types of congested regions, Variable Renewable Energy (VRE)-rich Areas and Load Centers, and explores likely plant configuration choices for developers and transmission network planners. This paper examines how hybrid value, comprising energy and capacity value, varies by plant configuration and congested region type considering factors such as storage duration, battery degradation, and ability to charge from the grid. We select plant locations from across the seven main U.S. independent system operators (ISOs). Hybrid value for each configuration is computed based on profit-maximizing plant operation given perfect foresight, according to observed wholesale power market real time prices from 2018 to 2021. In VRE-rich Areas, the median increase in energy value from extending storage duration from one to 4 h is 29.4 % for solar and 26.8 % for wind, assuming low battery degradation costs and storage sized to 100 % of the plant's nameplate generation capacity. Increasing storage duration beyond 4 h does not substantially increase its value from energy markets, even in VRE-rich Areas. We find that solar hybrids reach a 90 % capacity credit with 4 h of storage, while wind hybrids require 8 h of storage, based on the capacity factor of each hybrid during the top 100 net load hours.

Improved Message Mechanism-Based Cross-Domain Security Control Model in Mobile Terminals

Dual-domain terminal with two built-in independent operating systems - Life Domain and Work Domain, provides convenience for daily use and mobile office. However, the security isolation between the two domains also causes that message reminders cannot be delivered and viewed across domains, which restricts the improvement of work efficiency and the expansion of mobile services. This paper conducts an in-depth study on this pain point and proposes the concept and implementation method of a cross-domain instant messaging reminder service system for mobile office, focusing on solving the problems of: cross-domain isolated boundary exchange of message reminders, timeliness and delivery rate guarantee of message reminders, and security check filtering of message contents. Technically, on the side of mobile office platform, based on AMQP technical framework and protocol, the cross-domain isolated border message queue push and synchronization services are built, which are real-time, reliable and high-throughput.

Distributed optimization for EVs integrated power system considering flexible ramping requirement

More flexible ramping service is required due to the increase of renewable power generation in power systems. Electric vehicles (EVs) could provide such flexible ramping products (FRPs) at low cost while participating in the electricity market through aggregation. However, EVs’ dispatching capability cannot be fully utilized without the right incentives. This paper addresses a distributed optimal model developed between EV aggregators (EVAs) and the independent system operator (ISO). To make such concept, a cloud-edge collaborated market structure is adopted. At the edge level, EVAs assess the dispatching capability and solve the market bidding subproblem. At the cloud level, ISO solves the market clearing subproblem considering system economy and security. The overall problem is solved by the analytical target cascading (ATC) method. Heuristic constraints are also introduced into the model to improve convergence performance. The model is tested on a modified IEEE 30-bus system. Results demonstrate that the proposed method can incentivize EVAs with different owners to shift load and provide FRPs accurately, meanwhile reducing the cost and increasing the consumption of renewable energy effectively.

A pool-based electricity retail markets integrating renewable energy sources and electric vehicles in the presence of demand response program and conditional value at risk to enhance energy security

In restructured power systems, energy security has become a critical concern, driven by changes in laws and regulations and underscored by a defensive approach implemented by the independent system operator. This alignment highlights a strong interdependence between energy security, energy supply, and the economic aspects of energy management. The paper explores how consumers energy supply in restructured systems can be managed through Demand-Side Management (DSM) and Demand Response (DR) programs, considering economic efficiency and risk in scenarios that include Renewable Energy Sources (RES), suitable storage systems, and electric vehicles (EVs). An energy security index is introduced to evaluate the impact of DR programs on both electrical and thermal energy, considering the economic risks involved. A hybrid pool-based electricity retail market approach is utilized to optimize energy security, supply, and consumption. The significant roles of RES, storage systems, and EVs are highlighted in this context. Economic and environmental optimization is achieved using the Conditional Value at Risk (CVaR) method, facilitating strategic decision-making under varied risk conditions. The energy supply security is further analyzed in the presence of electricity market retailers, exploring different pricing strategies. A Mixed-Integer Programming (MIP) model, implemented in GAMS software with the CONOPT solver, is used to examine pricing methods such as Time-of-Use (TOU) and real-time pricing. Findings reveal that DR programs lead to substantial increases in retailer profit and energy supply security value, with real-time pricing showing a significant 23.5 % increase in energy supply security value and a 24.5 % increase in retailer profit compared to fixed tariff pricing.

Strategic bidding by predicting locational marginal price with aggregated supply curve

Price-makers in the generation sector can utilize offer data published by independent system operators (ISOs) for strategic bidding. Many research uses system-wide offer information for strategic bidding because individual offers are usually published with masked identifications. However, these methods are not applicable in markets with transmission congestion because the nodal information is not used. A research gap remains in using system-wide offer information and accessible nodal information for strategic bidding in markets with congestion. System-wide aggregated supply curves can be formed just with anonymous offers, and locational marginal price (LMP) is accessible nodal data containing the congestion information. This paper proposes a framework for price-makers to bid strategically by predicting LMPs with aggregated supply curves. A feature extraction method is proposed to make aggregated supply curves applicable for LMP prediction, and the maximal information coefficient is developed for feature selection. A convolutional neural network is combined with a long short-term memory network to model the impact of aggregated supply curves on LMPs. In this framework, price-makers can investigate the impact of their strategies on aggregated supply curves, predict LMPs with aggregated supply curves, and make the optimal bidding strategies. Numerical results based on the PJM-5 bus system and real data from the Midcontinent Independent System Operator validate the effectiveness of the proposed framework.

The Expansion of Incentive (Performance-Based) Regulation of Electricity Distribution and Transmission in the United States

I examine developments in the application of performance-based regulation (PBR) to electricity distribution and transmission in the United States. Applications of comprehensive PBR to electricity distribution had been slow to diffuse in the U.S. prior to roughly 2000. PBR mechanisms are now being applied more frequently to electricity distribution, which reflects the changing structure of the electric power industry and the increasing obligations that are being placed on electric distribution companies. The new obligations are a consequence primarily of aggressive targets for decarbonizing the electricity sector in nearly half the states and the goal of using “clean” electricity to electrify transportation, buildings, and other sectors. PBR should be viewed as a set of “building blocks” that can be adopted in various combinations and should recognize that PBR and traditional cost-of-service regulation (COSR) are properly viewed as complements rather than substitutes. Recent reforms in the regulation of distribution companies in Great Britain—“RIIO”—have been influential in the U.S. The main reforms contained in RIIO are discussed. There has been essentially no application of PBR by the Federal Energy Regulatory Commission (FERC) to owners of transmission assets or to independent transmission operators. FERC has applied targeted incentives to encourage investment in transmission facilities and membership in independent system operator organizations. However, the regulation of transmission rates relies primarily on COSR in the form of formula rates and has poor incentive properties. Regulation of independent system operators is a challenge because they are non-profit organizations with no equity to put at risk. Reforms here are suggested.

A short-term power load forecasting system based on data decomposition, deep learning and weighted linear error correction with feedback mechanism

Accurate power load forecasting enables Independent System Operators (ISOs) to precisely quantify the demand patterns of users and achieve efficient management of the smart grid. However, with the increasing variety of power consumption patterns, the power load data displays increasingly irregular characteristics, which posing great challenges for accurate load forecasting. In order to solve above problem, a novel power load forecasting system is proposed based on data denoising, customized deep learning and weighted linear error correction. Specifically, we first proposed an improved optimization algorithm IGWO-JAYA which enhanced the Grey Wolf Optimizer (GWO) algorithm by using Halton low-discrepancy sequence and the mechanism of JAYA algorithm. In data denoising, the proposed optimizer was employed to optimize the Variational Mode Decomposition (VMD), enabling data-driven intelligent denoising. The customized deep learning framework contained multi-layer Convolution Neural Network (CNN), Bi-directional Long Short-Term Memory (Bi-LSTM) and Multi-Head Attention mechanism. The effective integration of these layers can significantly improve the capacity for nonlinear fitting of deep learning. In weighted linear error correction, the IGWO-JAYA algorithm was employed to determine the appropriate weight for point forecasting values and residual forecasting values. By weighting them, the forecasting precision has been further enhanced. To verify the forecasting ability of the system, we conducted experiments on power load datasets from four states in Australia and found that it has the best performance compared with all rivals. In the discussion, we demonstrated the convergence efficiency of the IGWO-JAYA algorithm by CEC test function.

The spillover effect of mandatory renewable portfolio standards

Renewable Portfolio Standards (RPSs) are one of the most prevalent and impactful clean energy policies implemented by states in the United States. This paper investigates the regional spillover effect of RPS policies using a directed dyad panel dataset of renewable electricity generation in US states from 1991 to 2021. Regional spillover effect is measured in two ways: by considering the influence of an RPS enacted in neighboring states and in states in the same regional transmission organization or independent system operator region. We use dyadic fixed effects estimation and conclude that the neighboring state’s RPS stringency score is a strong determinant of a state’s total renewable electricity generation. For states without an RPS, the positive influence of an RPS in a neighboring state is larger when the non-RPS state has more abundant renewable energy resources than the neighboring RPS state. Our findings suggest that past RPS policy evaluation research using a confined within-state focus may have underestimated the holistic impact of an RPS, as the impacts of an RPS policy can extend beyond the enacting state’s borders. Overall, this study contributes to an improved understanding of the holistic impact of state RPS policies.

Data-Driven Residential Electric Vehicle Charging Behavior and Load Profile Modeling for Demand Response in the Midcontinent Independent System Operator Region

Data-Driven Residential Electric Vehicle Charging Behavior and Load Profile Modeling for Demand Response in the Midcontinent Independent System Operator Region