Flexible Ramping Research Articles

Probabilistic Power and Energy Balance Risk Scheduling Method Based on Distributed Robust Optimization

The volatility and uncertainty associated with the high proportion of wind and PV output in the new power system significantly impact the power and energy balance, making it challenging to accurately assess the risks related to renewable energy abandonment and supply guarantee. Therefore, a probabilistic power and energy balance risk analysis method based on distributed robust optimization is proposed. Firstly, the affine factor and the flexible ramp reserve capacity of thermal power are combined to establish a probabilistic index, which serves to characterize the risk associated with the power and energy balance. Drawing upon the principles of the conditional value at risk theory, the risk indexes of the load shedding power and curtailment power under a certain confidence probability are proposed. Secondly, the probability distribution fuzzy sets of uncertain variables are constructed using the distributionally robust method to measure the Wasserstein distance between different probability distributions. Finally, aiming at minimizing the operation cost of thermal power, the risk cost of power abandonment, and the risk cost of load shedding, a distributed robust optimal scheduling model based on a flexible ramp reserve of thermal power is established.

A two-stage distributed stochastic planning method for source-grid-load-storage flexibility resources considering flexible ramp capacity

With rapidly increasing levels of renewable energy penetration, flexibility resources play an ever more critical role in the future power system. This paper describes a two-stage stochastic mixed integer second order cone (MISOC) method for flexibility resource planning (VFRP) considering source-grid-load-storage interactions. Considering renewable energy penetration levels and flexibility resource characteristics, this study quantifies the extent to which the share of renewable energy generation affects cost performance, and assesses the priority and potential of investing in flexibility resources. Moreover, the multi-type flexibility resource portfolio allocation method and energy storage only approach are compared in terms of socio-economic benefits. A multi-cut Benders-based approach is used for decomposing large-scale stochastic expansion planning problems to reduce the computational burden. Numerical simulation tests on the modified energy test systems I39G20 and F213G20 verified the correctness and effectiveness of the proposed flexible resource planning models and solution algorithms. The simulation results show that retrofit of coal-fired units for better flexibility has a higher investment return. Still, energy storage presents a tremendous investment prospect and potential. Finally, the multi-type flexibility resource portfolio allocation method is more cost-effective than an energy storage only approach.

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.

Optimal scheduling of a community Multi-Energy system in energy and flexible ramp markets considering Vector-Coupling storage Devices: A hybrid Fuzzy-IGDT/Stochastic/Robust optimization framework

Community multi-energy systems (CMESs) are emerging as a pivotal solution to address the pressing issues of the current energy crisis and environmental pollution. This paper introduces a comprehensive multi-horizon risk-averse scheduling strategy for CMESs, targeting participation in energy and flexible ramp markets while meeting the demands of electric, natural gas, and hydrogen vehicle charging stations, data centers, and residential and commercial buildings. The proposed CMES incorporates power-to-x vector coupling storage (PtX-VCS) systems, enabling the storage of electricity in various forms to cater to multi-energy demands. Additionally, an integrated demand response (IDR) program, incorporating incentive and load-shifting strategies, is applied to enhance operational efficiency and system flexibility. To manage uncertainties effectively, a hybrid stochastic/robust/fuzzy information gap decision theory (IGDT) method is employed. This approach, formulated as a tri-level optimization model, is transformed into a single-level model using strong duality theory. The results demonstrate a notable reduction in daily operational costs by approximately 5% and 4% through the implementation of the IDR program and PtX-VCS technologies, respectively. Moreover, the CMES’s potential to participate in the flexible ramping market leads to an approximately 7% reduction in total operational costs.

A Joint Electricity Market-Clearing Mechanism for Flexible Ramping Products with a Convex Spot Market Model

A high proportion of renewable energy access makes the net load of the power system volatile and uncertain, increasing the demand for the ramping capacity of the power system. Traditional electricity spot markets compensate for the power imbalances caused by an insufficient ramping capacity through traditional flexibility services such as ancillary services and interconnection power. However, conventional flexibility services may lead to frequency deviations in the power system, increased response costs, spikes in electricity prices, and dramatic price volatility in the traditional spot market. To solve the above problems, this paper proposes an FRP and convex electricity spot market joint clearing (FCESMJC) market mechanism. The FCESMJC model can more accurately represent the relationship between electrical power output and the price of electricity and reduces the number of spikes in electricity prices. In addition, a novel FRP pricing method is proposed to compensate FRP market participants for their FRP costs more reasonably. Additionally, the difference in system performance is provided by comparing the energy prices, pricing method, clearing prices, and system costs in the FCESMJC method and the traditional electricity spot market. The FCESMJC system reduces the total system cost by 18.6% compared with the electricity spot market. Numerical experiments are simulated on the IEEE 14-bus test system to validate the superiority of the proposed model.

Wind Power Deviation Charge Reduction using Machine Learning

High penetration of wind power plants in power systems resulted in various challenges such as frequent system imbalances due to highly uncertain and variable wind generation. Additional spinning reserves and specific balancing products such as flexible ramp products are used to handle such frequent imbalances. Incorporation of these ancillary services leads to increased total operational costs. Increased operational costs should be transferred to wind power producers as it is caused by wind power plants. This leads to penalizing the wind power producers for the deviation of power generation from forecasts, called deviation charges. These deviation charges can be reduced by improving the forecasting accuracy. Existing forecasting models show performance in terms of error matrices. Such error matrices do not indicate the financial loss associated with it. This can be overcome by expressing forecasting performance in terms of deviation charge and it will directly encourage wind power producers to improve forecasting accuracy or arrange reserves to accommodate the error. This paper proposes a backpropagation-based artificial neural network model for reducing deviation charges in this context. An analysis is conducted on the data collected from the Bonneville Power Administration (BPA) Balancing Area. Seasonal analysis (Spring, Summer, Fall, and Winter) is conducted to show the performance of the proposed model throughout the year. The proposed model performance is compared with linear regression and ARIMA models. The comparison shows that the proposed ANN model gives the least deviation charges in the Spring, Summer, and Winter seasons and deviation charges in the Fall season are higher than the ARIMA model.

Network-constrained flexible ramping product provision of prosumer aggregator: a data-driven stochastic bi-level optimization

Prosumers are expected to provide the flexible ramping product (FRP) in the power system. However, voltage violations and line congestion may arise in the distribution network, when FRP delivered by prosumers. Hence, this paper proposes a data-driven stochastic bi-level optimization model to coordinate the prosumer aggregator to decide FRP-offering while ensuring distribution network security under FRP delivery. In the proposed bi-level model, the upper-level is a min-max problem, representing the minimum expected cost under the worst-case scenario probability distribution for the prosumer aggregator. The lower-level is the operation cost minimization within the distribution network security for distribution network operator. The proposed model is converted into a single-level model using the Karush-Kuhn-Tucker condition and strong duality theory, and applied to the modified IEEE 33-bus network with three prosumers. The results demonstrate the effectiveness of the proposed model.

Multi-Time-Scale Rolling Optimal Scheduling of Virtual Power Plants in Energy and Flexible Ramping Product Markets

Virtual power plants (VPPs) offer a feasible solution for integrating various types of distributed energy resources (DERs) into the power grid in the electricity market. This paper proposes a multi-time-scale rolling optimal scheduling for VPPs, enabling optimal self-scheduling plans across intra-week rolling scheduling, intra-day rolling dispatch, and real-time dispatch. The proposed method facilitates participation in energy and flexible ramping product (FRP) markets while considering the specific characteristics with complementary advantages for the aggregated renewable resources, gas turbines, energy storages, and flexible demands of each time scale. The flexible ramping products within the VPP framework are established, while the energy storage systems address fluctuations during dispatch periods. Case studies are conducted to verify the efficiency of the proposed method. The results show that the proposed method can obtain optimal self-scheduling plans within the multi-time framework and has better performance in economy and security operation in comparisons among cases.

Power-Gas Coordination: Making Flexible Ramping Products Feasible

The power industry has introduced flexible ramping products (FRPs) to improve the operational flexibility that high penetration of renewables requires. However, FRPs are mainly provided by natural-gas-fired power generators, whose capability to provide ramping service is determined by the operational conditions of the natural gas system. We develop a power-gas coordination model that takes into account FRP requirement. Specifically, the power market represents the required energy-FRP co-optimization, while the natural-gas market is settled considering FRP-deployment gas constraints. A Lagrangian coordination scheme is used to identify a market equilibrium involving the two markets that are settled separately. Computations results on two test systems examine the impact of gas-system operating constraints on the delivery of FRPs. Additionally, sensitivity analyses with respect to gas-network bottlenecks, FRP price caps, and renewable penetration levels are presented.

Coordinating flexible ramping products with dynamics of the natural gas network

In electricity networks with high penetration levels of renewable resources, Flexible Ramping Products (FRPs) are among the utilized measures for dealing with the potential fluctuations in the net demand. This paper investigates the coordinated operation of Integrated Electricity-Gas Systems (IEGSs) with considering FRPs. To accurately model and reflect the effects of variations in the natural gas fuel demand on the natural gas network, a dynamic Optimal Gas Flow (OGF) formulation is utilized. The non-convex dynamic model of the natural gas system is represented in a convex form via a tight relaxation scheme. An improved distributed optimization method is proposed to solve the coordinated operation problem in a privacy-preserving manner, where the two infrastructures only share limited information. We introduce the Inexact Varying Alternating Direction Method of Multipliers (IV-ADMM) and show that compared with the classic ADMM, it converges considerably faster and in fewer iterations. Through a comparison of day-ahead and real-time operation planning results, it is concluded that without accounting for natural gas network dynamics, the FRP model is not a trustworthy tool in day-ahead planning against uncertainties.

A Power External Transmission Strategy for Regional Power Grids Considering Internal Flexibility Supply and Demand Balance

This paper provides an optimization strategy for flexible operation at the system level to guide the real-time flexible ramping of the power grid. Firstly, the concept of regional power grid flexibility is clarified, and the ramping factor is proposed as a flexibility metric. On this basis, taking the output priority of each node as the objective function and considering constraints such as line flow, power balance, and system flexibility, a mathematical model for regional power grid transmitting flexible power externally is proposed. Compared with the traditional models, this model focuses more on flexibility rather than economy. A case analysis based on the improved IEEE 30-Bus System verifies the feasibility and effectiveness of the proposed strategy and its advantages over the traditional models.

Secure expansion of energy storage and transmission lines considering bundling option under renewable penetration

This paper presents a multi-stage expansion model for the co-planning of transmission lines, battery energy storage (ES), and wind power plants (WPP). High penetration of renewable energy sources (RES) is integrated into the proposed model concerning renewable portfolio standard (RPS) policy goals. The possibility of bundling existing transmission lines to uprate power flow capacity is considered. Renewable energy curtailment and load shedding are included in the model to assess the system operation more precisely. Battery ES devices are co-planned to defer transmission expansion and renewable management. To make the time complexity of the problem tractable and capture the uncertainties of load and RES in an hourly resolution, a chronological time-period clustering algorithm is used to extract the representative hours of each planning stage. Additionally, the flexible ramp reserve is utilized to handle the uncertainty of RES. An accelerated Benders dual decomposition (BDD) algorithm is developed to solve the proposed model mixed-integer linear programming (MILP) formulation. The N-1 security criterion is evaluated by considering a designed contingency screening (CS) algorithm to identify higher risk contingencies. The effectiveness of the proposed co-planning model is evaluated using IEEE RTS 24-bus and IEEE 118-bus test systems.

Tight power balance multi-time scale disposal strategy for wind integrated system considering electric vehicle charging station

High wind power penetration and peak load pose significant challenges to the power system in maintaining the power supply–demand balance. Once the power supply–demand balance is disrupted, system load shedding and wind spillage become inevitable. In this study, the aforementioned problem is defined as the tight power balance (TPB) problem. The accuracy of wind power and load forecasting increases with a decrease in the forecasting timescale. Based on the aforementioned characteristics, a TPB multi-timescale disposal strategy for wind-integrated power systems considering electric vehicle (EV) charging stations is established to address the TPB problem at different timescales. First, the operation model of an electric vehicle charging station offering flexible ramping capacity is established. Second, a multi-timescale disposal strategy, which includes an intra-day 4-h plan, an intra-day 1-h plan, and a real-time 15-min plan, is presented by quantifying the flexible demand at different timescales. Finally, the proposed strategy is verified using a modified IEEE 118-bus system. The analysis results show that the proposed TPB multi-timescale disposal strategy effectively promotes the disposal level of the TPB problem.

Distributionally robust comprehensive declaration strategy of virtual power plant participating in the power market considering flexible ramping product and uncertainties

The volatility and randomness of renewable energy output make the flexible ramping ability demand of power system more urgent, and flexible ramping product (FRP) can effectively improve the flexibility of the power system. Virtual power plant (VPP) can regulate distributed generation, which is conducive to the regulation potential of flexible resources. To solve the problems of FRP and electric energy markets synergy and the wind power output uncertainty faced by the VPP in the declaration process, this paper proposes a comprehensive declaration strategy for the VPP to participate in the power market considering FRP and uncertainties. Firstly, this paper designs a synergistic trading mode covering flexibility resource demand determination and the virtual bidding curve formation for electric energy and FRP markets, in which the virtual bidding curve can reasonably compensate for the unit opportunity cost. Secondly, a comprehensive declaration-dispatching strategy decision-making model for VPP is constructed, and a two-stage distributed robust optimization (DRO) technology is used to deal with the wind power output uncertainty in the model, and flexible resources such as energy storage are used to mitigate energy deviation in the VPP. Finally, simulations implemented on a typical VPP are delivered to show that: 1) Virtual bidding curve realizes the accurate compensation for the units providing FRP. 2) Compared with the single market, VPP can increase the expected profit by 20.44% and reduce wind curtailment cost by 59.68% in the joint declaration of multi-markets.3) VPP can effectively suppress output uncertainty through energy storage system. 4) DRO model has significant advantages in data-driven, less conservative results and stable running time.

Day-Ahead Optimal Scheduling of Resources in a Distribution System in Joint Energy, Regulation and Ramp Markets

The increasing trend of integrating Renewable Energy Sources (RESs) into power systems could cause operational challenges in the system from flexibility and reliability perspectives. Respectively, utilities would be relied on local flexible resources to address the flexibility requirements in power systems. In this respect, local resources in distribution systems could participate in ancillary markets along with the wholesale energy markets to address the flexibility capacity shortage in the system. Consequently, this paper aims to provide a framework to optimize the scheduling of resources in distribution networks while participating in energy, regulation, and ramp markets. Correspondingly, this approach would maximize the profits of the local resources in distribution systems whereas providing Flexible Ramp Product (FRP) and regulation reserve for the system operators. Respectively, an optimized bidding strategy is developed to maximize the profits of the local resources, while modeling the operational constraints of the distribution grids and power losses to improve the accuracy of the proposed scheme. Furthermore, the chance constrained concept is taken into account in the proposed scheme to model the uncertainty of RESs. Finally, the model is applied on the IEEE-37-bus-test-system and the sensitivity analysis is employed to investigate its effectiveness in the management of the system.

Improving the flexibility of power systems using transportable battery, transmission switching, demand response, and flexible ramping product market in the presence of high wind power

AbstractWith the increasing demand for electricity and the significant growth of renewable energy sources (RES), especially wind power, the need for new services to increase system flexibility has increased more than ever. Flexibility services can enhance system security. This paper proposes a framework for enhancing system flexibility by integrating emerging services for running the day‐ahead energy, spinning, and flexible ramping product (FRP) markets, considering high wind power penetration. Due to the uncertainty of wind power production, this framework is presented as a stochastic network‐constraint unit commitment (NCUC) problem. The services that are implemented simultaneously in the problem include transmission switching (TS), demand response (DR) program, and transportable battery energy storage (TBES). Moreover, two new indices are introduced to evaluate the effects of flexibility services. Another contribution of this paper is performing comprehensive studies for comparing various case studies in normal and the worst contingency conditions of the network. The IEEE RTS‐79 test system is employed to approve the effectiveness of the proposed framework. The results of various case studies show that using emerging services together increases power grid flexibility and reduces system operating costs.

Two-Layer Generation Expansion Planning Based on Flexibility Balance

Reasonable planning of flexible power sources is crucial for the reliable operation of new power systems based on new energy sources. However, in the long-term planning, how to consider the short-term ramping capability of the flexible power supply to meet the capacity demand of the net load and the flexible ramping requirement at the same time is still an unsolved problem. Therefore, this paper conducts research on generation expansion planning based on flexibility balance. Firstly, the flexibility balance are modeled based on the flexible supply domain and the net load fluctuation domain, which are used as the flexible supply and demand constraints in the planning stage. Then, a two-layer expansion planning model is constructed, including two modules of investment decision and production simulation. Flexibility index is proposed to quantify the system’s ability to adjust at different levels of payload, and incorporated into the planning model for iterative optimization. Finally, an example is verified on the improved IEEE-39 node system. The test results show that the long-term generation expansion planning scheme based on the balance of flexible supply and demand can effectively meet the net load demand and its ramping demand.

Machine Learning Predictions of Electricity Capacity

This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This research accomplishes these aims. The models built in this paper identify wind forecast, sunrise/sunset and the hour of day as primary predictors of net load imbalance, among other variables, and show that the average size of the INC and DEC capacity requirements can be reduced by over 25% with the margin of error currently used in the industry while also significantly improving closeness and exceedance metrics. The reduction in INC and DEC capacity requirements would yield an approximate cost savings of $4 million annually for one of nineteen Western Energy Imbalance market participants. Reconstructability Analysis performs the best among the machine learning methods tested.

Provision of the Flexible Ramping Product in a microgrid considering the trading strategies in the energy markets

The Flexible Ramping Product (FRP) market has been created as a new ancillary service market for the power systems to manage the severe ramp rates in the net load of the system. Microgrids (MGs) can provide the required ramp service for this market. The MG manager (MGM) sends its bids to the FRP market. The mutual effects of these bids on the MGM trading strategies in the day-ahead (DA) and real-time (RT) energy markets have not been considered in the previous studies. For this purpose, a new formulation is developed in this paper to model the MGM decisions in the FRP market while participating in the DA and RT energy markets. Moreover, the uncertainty on the probability of bid acceptance in the FRP market is modeled in the MGM decision-making problem using the information gap decision theory approach. The results show that changing the MGM strategies in participating in the markets, the objective function of the MGM improves when it participates in the DA energy, FRP and RT energy market, in comparison with the cases in which the MGM does not participate in the FRP market or the RT energy market. Also, the results show how the risk-averse MGM changes its strategies to provide the ramp service for the FRP market to make its decisions robust against the uncertain parameter.

A transaction mechanism for flexible ramping products with the participation of hydropower

A transaction mechanism for flexible ramping products with the participation of hydropower