Ramping Requirements Research Articles

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.

Optical design and higher-order aberrations with the superconducting gantry based on limited ramping fields

One of the major challenges in proton therapy is the cost and scale of the treatment facility, which depends mainly on the dimensions of the gantry. A potential method that promises to make the gantry lighter and more compact is superconducting magnet technology. However, the rapid ramping requirements during energy modulation are the primary reason for magnet quenching. To alleviate these restrictions, we propose a superconducting bending section consisting of a TBA lattice for a gantry optical design. With only three magnetic field settings needed to deliver 70–200 MeV proton beams, it achieves ±10% large momentum acceptance owing to its significant dispersion function suppression capabilities. Another feature that contributes to reducing the footprint is the installation of a degrader on the proposed gantry, allowing the degraded beam to be guided directly to the isocenter without being filtered by the Energy Selection System. For verifying the feasibility of the concept, we assess the possibility of beam scraping in the beam path and beam spot distortion at the isocenter. These efforts are accomplished by modeling realistic field distributions with ANSYS Maxwell and then performing 7th-order transfer mapping through COSY Infinity. Regarding the influence of broad energy spread on beam depth dose distribution, TOPAS is used to simulate and compare the integrated depth dose for various therapeutic energies.

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.

Hydropower flexibility valuation tool for flow requirement evaluation

Timing of generation is becoming more and more valuable. This creates greater potential tension between environmental and power system objectives since both systems require their own flow patterns. Identifying win–win outcomes in this context requires being able to discuss the value of flexibility across stakeholder groups. This research proposes a two-stage optimization method to understand hydropower flexibility to meet both environmental and power system requirements. The tool simulates the two-settlement market process in the U.S. by maximizing revenues from both the day-ahead and real-time markets, subject to plant operational limits, regulatory flow and ramping requirements, and uncertainties associated with water availability and market prices. The model is formulated as linear programming problems and solved using IBM ILOG CPLEX optimizer. By examining a range of flow requirements, ramping constraints, and storage capacities, the proposed tool shows how to make more informed decisions to weigh the cost of specific flow requirements in the context of the overall license requirements. Results from the case study show that revenue is more sensitive to the ramping constraints than the minimum flow constraints. We also demonstrate that removing flow constraints in a dry month increases monthly revenue by up to 118%, as opposed to only 1% in a wet month. In addition, our results suggest that using a learning-based water flow forecast results in an increase of monthly revenue up to 6.4% compared with persistence forecast.

Multi interval Zero Carbon Flexible Ramp Product

Traditional Fossil Fuel Generators (FFGs) are incapable of providing Flexible Ramp Product (FRP) due to lower FRP market time frame, while modern FFGs are characterized by high ramping costs. Also, ramping support providence from FFGs enhance carbon emissions. Wind and solar FRP can reduce system imbalances, however, their ramping capabilities are highly uncertain and may not reliably fulfill System Ramping Requirement (SRR). This paper proposes a reliable and deliverable Zero Carbon Flexible Ramp Product (ZCFRP) using wind, solar and Variable Speed Pumped Hydro Power Plants (VSPHPPs). The reliability of the proposed ZCFRP depends on the estimation of accurate SRR, Renewable FRP (RFRP) availability, and generation/pumping capacity of VSPHPP. SRR and RFRP availability are estimated using copula theory. VSPHPP’s generation/pumping capacities are optimally modeled considering historical SRR and maximum possible RFRP variation. Delivery of the proposed ZCFRP is ensured by considering transmission line constraints. Dynamic ramping constraints are considered to reflect the practical characteristics of thermal generators. A case study shows that the proposed ZCFRP is beneficial to both system and ZCFRP providers (ZCFRPPs) by reducing dispatch costs and increasing revenue, respectively.

Capacity and Intra-Hour Ramp Reserves for Wind Integration

We propose a power-based unit commitment formulation with capacity and intra-hour ramp reserves for dealing with intra-hour wind power variability and uncertainty. Although the formulation has an hourly resolution, the intra-hour ramp requirements capture wind power ramp excursions with a time duration below one hour, and thus allows the formulation to consider intra-hourly wind variability and uncertainty. This increases the security of the formulation compared to using hourly ramp reserves and allows more efficient scheduling of units with high ramp rates. We test the formulation with different durations for the intra-hour reserves, and using both hourly and intra-hourly ramp reserves, to find the best reserve formulation. The formulations are evaluated using a 5-min economic dispatch, which simulates the real-time operation of the system, for hundreds of out-of-sample realizations of wind power production. The proposed formulations are then compared to two hourly stochastic formulations and a stochastic formulation with 5-min time resolution. The proposed formulations outperform the stochastic formulations in terms of security, showing that they provide a scheduling which is more robust against intra-hour wind power variations. The proposed formulations also outperform the hourly stochastic formulations in terms of total costs, giving a better trade-off between scheduling costs and security.

Sizing ramping reserve using probabilistic solar forecasts: A data-driven method

Ramping products have been introduced or proposed in several U.S. power markets to mitigate the impact of load and renewable uncertainties on market efficiency and reliability. Current methods often rely on historical data to estimate the requirements of ramping products and fail to take into account the effects of the latest weather conditions and their uncertainties, which could lead to overly conservative or insufficient requirements. This study proposes a k-nearest-neighbor-based method to give weather-informed estimates of ramping needs based on short-term probabilistic solar irradiance forecasts. Forecasts from multiple sites are employed in conjunction with principal component analysis to derive numerical classifiers to characterize system-level weather conditions. In addition, we develop a data-driven method to optimize the model parameters in a rolling-forward manner. By using real-world data from the California Independent System Operator, we design two metrics to evaluate method performance: 1) frequency of shortage and 2) oversupply of ramping product. Our proposed method presents advantages in comparison with the baseline and a set of benchmark methods: without compromising system reliability, it reduces system ramping requirements by up to 25%, therefore improving both system reliability and economics.

Using probabilistic solar power forecasts to inform flexible ramp product procurement for the California ISO

How can independent system operators (ISOs) take advantage of probabilistic solar forecasts to lower generation costs and improve reliability of power systems? We discuss one three-step approach for doing so, focusing on how such forecasts might help the California Independent System Operator (CAISO) prepare unexpected net load ramps, where net load equals gross demand minus wind and solar production. First, we enhance an existing solar forecasting system to provide well-calibrated hours-ahead probabilistic forecasts. We then relate the degree of uncertainty reflected in the forecasted prediction intervals (independent variables) to error distributions for net load ramp forecasts for the CAISO real-time market (dependent variable) using machine learning and quantile regression. Projected ramp forecast errors conditioned on solar uncertainty are translated into flexible ramp requirements that therefore reflect real-time meteorological and solar conditions, improving on typical ISO procedures. Detailed descriptions are provided on the quantile regression and kth-nearest neighbor categorization methods for accomplishing that translation. Finally, a multiple time-scale look-ahead market simulation model is applied to a 118-bus IEEE Reliability Test System, modified to represent the CAISO generation mix and demand distributions. The model runs quantify how solar-conditioned ramp requirements can, first, decrease operating costs by reducing requirements compared to often conservative unconditional methods and, second, decrease generation scarcity events and consequently improve reliability by increasing flexibility requirements at times when unconditional forecast-based requirements understate actual ramp uncertainty. Solar-conditioned ramp requirements are found to reduce generation operating costs by about 2% for the test system (which would be equivalent to over $100 million per year for a CAISO-size system).

Indirect cost of renewable energy: Insights from dispatching

The rapidly falling costs of renewable energy has made them the focus of efforts in making a low-carbon transition. However, when cheap large-scale energy storage is not available, the variability of renewables implies that fossil-based technologies have to ramp up-and-down frequently to provide flexibility for matching electricity demand and supply. Here we provide a study on the indirect cost of renewable energy due to thermal efficiency loss of coal plants with such ramping requirements. Using monthly panel data for China, we show that higher renewable share is associated with fewer operating hours of coal-fired units (COHOUR). We use an instrumental variable depending on natural river flows to identify the causal effect of reduced COHOURs in raising the heat rate of coal-fired units. Specifically, a 1 percentage point increase in the share of renewables leads to a 6.4 h reduction per month, and a reduction of one COHOUR results in a 0.09 gce/kWh increase of gross heat rate (+0.03%). We estimate that the thermal efficiency loss indicates 4.77 billion US dollars of indirect cost of renewables in 2019, or 9.44 billion if we include the social cost of carbon emissions. These results indicate that we should consider the indirect impacts of renewables on total coal use and the importance of increasing flexibility of the system.

A hybrid multiobjective optimization technique for optimal sizing of BESS‐WtE supported multi‐MW HRES to overcome ramp rate limitations on thermal stations

In India, favorable renewable energy policies have resulted in a higher proportion of renewable energy in the system. However, this led to the lower capacity utilization factor of thermal power stations. Any additional burden on thermal stations by enforcing higher ramp rates during peak hours would seriously affect technically as well as economically. In this regard, further renewable additions must be supported by energy storage systems to meet the ramping requirements. Waste-to-energy (WtE) plants play a crucial role in improving grid resilience in a high-renewable energy scenario to support expensive battery energy storage systems. In this article, the potential of WtE is assessed for a smart city in India, selected based on the possible urban and industrial growth, along with three other renewable energy technologies such as floating solar, bifacial rooftops solar, and wind energy conversion systems. The selected location has a total renewable energy potential of 439 MW. To achieve the municipality's techno-economic objectives, a multiobjective-enabled adaptive local attractor–based quantum-behaved particle swarm optimization (ALA-mQPSO) is proposed with mutation. With the proposed ALA-mQPSO, Pareto optimal sets of the hybrid renewable energy sources aided by the combination of battery energy storage system and WtE plant is achieved. The Pareto fronts are then compared with the benchmark techniques such as differential evolution along with the recently proposed multiobjective golden eagle optimizer algorithm. The results show that with the addition of the WtE plant, the grid can provide greater reliability for an optimal set of BESS and HRES. The obtained optimal configuration resulted in a levelized cost of $0.0539 along with just 0.049% of loss of power supply probability and 0.048 cycle loss of BESS, and when compared with similar works from the literature, the results proved to be superior and realistic.

Decentralized approach for security enhancement of wind‐integrated energy systems coordinated with energy storages

Wind speed fluctuations in wind-integrated energy systems render the need for the fast and proper response of the conventional thermal generating units. This is while ramping constraints of these units as the backbone of the traditional power generation cycle depreciate their response, which poses a serious security challenge. This article is aimed at enhancing the security of multiarea wind-integrated energy systems by the coordinated deployment of energy storage systems (ESSs). To realize a practical model, a probabilistic reformulation of decentralized security-constrained optimal power flow by taking into account the thermal units' ramping constraints is developed, which tackles the uncertainties of wind speed fluctuations through a proper provision of ramping requirements by ESSs. Moreover, the optimality condition decomposition algorithm is used to solve security-constrained optimal power flow at each area of the energy system in a decentralized manner. Furthermore, to reduce the computational burden, a contingency filtering approach is employed that filters the uncritical conditions during the solution process. Different scenarios of transmission line and generator outages are explored by linear outage factors, including the line outage distribution factor and the power transfer distribution factor. In the established model, the value of loss of load and the value of wind curtailment factors are employed to reflect the economic impact of load shedding and wind energy curtailment. For supplying the lost power in generation outages, the participation factor of the connected sources is modified, which considers the remained generation capacity of sources. To assess the efficiency of the proposed model, the New England 39-bus testbed is put under extensive investigation. The results are discussed in depth.

Multi-objective mutation-enabled adaptive local attractor quantum behaved particle swarm optimisation based optimal sizing of hybrid renewable energy system for smart cities in India

Favourable renewable energy policies led to a higher proportion of renewable energy in the country’s entire energy generation. However, these regulatory affairs caused the lower capacity utilisation factor of thermal power stations. It is not prudent to burden the thermal stations by enforcing ramp rates during peak hours as this would cause serious operational issues. Hence, further renewable additions must be supported by battery-energy storage systems (BESS) to meet the grid ramping requirements. BESS being quite expensive and require heavy maintenance, municipal solid waste-based waste-to-energy (WtE) plants play a crucial role in enhancing grid reliability under a high renewable energy (RE) environment. In this paper, the potential of four renewable energy technologies, such as floating solar, bifacial rooftop, wind energy conversion system, and WtE plant, are evaluated for a smart city, Visakhapatnam, India. This paper identifies a total of 439 MW of RE potential in the selected location. Mutation-enabled adaptive local attractor-based quantum behaved particle swarm optimisation (ALA-QPSO), supported by the combination of BESS and WtE plant, and is applied to obtain the optimal configuration of photovoltaic (PV) and wind energy conversion system (WECS) by minimising the techno-economic objectives of a municipal body. The results are compared with four variants of differential evolution. The results show that for an optimal set of BESS and hybrid renewable energy sources (HRES), the grid can offer better reliability with the presence of the WtE plant. The obtained optimal configuration results in a levelized cost of US $ 0.0539, high reliability with a loss of power supply probability of 0.049%, and reduced battery degradation with minimal life cycle loss of 0.048. The municipality requires a total investment of US $ 40.5 M, a significant part of which can be met by acting as a renewable energy service company between the rooftop owners and the distribution companies.

Enhancing power systems operational flexibility with ramp products from flexible resources

Flexible Ramp Products (FRPs) can address netload variability and uncertainty caused by increasing renewable energy integration. They secure additional ramping capability to follow netload variations of upcoming dispatch intervals. The challenges in current practice of FRP procurement are - 1) Neglecting start-time uncertainty of netload results in inaccurate ramp requirement estimation 2) Inadequate ramping capacity in real-time market for FRP provision leads to the commitment of out-of-merit units 3) Priority in dispatch of renewable energy sources de-commits Conventional Generation (CG) units, thus reducing available ramping capacity. Hence, scheduling FRPs in day-ahead market from CG units and flexible resources along with an appropriate estimation of FRP requirement is essential to ensure ramping sufficiency. In this context, the paper proposes a modified start-time and end-time netload uncertainty based FRP design and a two-stage scheduling framework to integrate FRPs in day-ahead market. Further, system-wide benefits of integrating flexible resources, FRPs and improved operations are highlighted. The proposed framework is simulated on IEEE RTS-24 and reduced Great Britain test systems and proficiency of the method is compared with existing FRP designs. Numerical results indicate a reduction in operating costs and ramping insufficiency with coordinated operation of flexible resources and FRP deployment.

An Investigation into the Potential of Hosting Capacity and the Frequency Stability of a Regional Grid with Increasing Penetration Level of Large-Scale PV Systems

It is widely believed that the incorporation of renewable energy to the current power grid is the way forward in achieving sustainable power generation. Currently, with the reduction of PV prices, many countries have started connecting PV systems into their grid network, hence leading to a sharp increase of the penetration levels of renewable electricity production. This will bring significant change in the load pattern and the ramping requirements of the grid’s conventional generation system due to the varying nature of the renewable energy generation. This significant change affects the stability of the grid frequency because it becomes more challenging for the system operators to maintain the equilibrium between the generation and load. Additionally, this significant change affects the PV system potential hosting capacity of the traditional grid because of the PV system’s curtailment in order to comply with the constraints of the grid’s conventional generation system. In this paper, the net load, grid frequency stability, and grid potential hosting capacity are evaluated in the situation of increasing the penetration level of large-scale PV systems generation into the grid. The results show that the grid operators will face increasingly variable net load patterns and steeper ramping events as the PV system penetration level increases. Additionally, the results show the requirement of having flexibility measures that target each grid constraint as the PV system penetration level increases.

Analytical solution of stochastic real‐time dispatch incorporating wind power uncertainty characterized by Cauchy distribution

Real-time power dispatch can coordinate wind farms, automatic generation control units and non-automatic generation control units. In real-time power dispatch, the probable wind power forecast errors should be appropriately formulated to ensure system security with high probability and minimize operational cost. Previous studies and the authors' onsite tests show that Cauchy distribution effectively fits the “leptokurtic” feature of small-timescale wind power forecast errors distributions. In this paper, a chance-constrained real-time dispatch model with the wind power forecast errors represented by multivariate Cauchy distribution is proposed. Since the Cauchy distribution is stable and has promising mathematical characteristics, the proposed chance-constrained real-time dispatch model can be analytically transformed to a convex optimization problem considering the dependence among wind farms’ outputs. Moreover, the proposed model incorporates an affine control strategy compatible with automatic generation control systems. This strategy makes the chance-constrained real-time dispatch adaptively take into account both the potential power ramping requirement and power variation on transmission lines caused by the generation adjustment to offset the wind power forecast errors in real-time power dispatch stage. Numerical test results show that the proposed method is reliable and effective. Meanwhile it is very efficient and suitable for real-time application.

Data-driven stochastic unit commitment considering commercial air conditioning aggregators to provide multi-function demand response

This paper proposes a two-stage data-driven unit commitment dispatching scheme, which coordinates the multi-function demand response of commercial air conditioning aggregators with thermal units to encounter the variations of load and wind power. In the proposed dispatching scheme, the flexible ramping capability of the thermal units is reflected in the coupling constraints between the ramping capacity and reserve capacity. To improve the flexibility of the whole system, multi-function demand response of commercial air conditioning aggregators is incorporated into the scheduling in both stages. In the day-ahead stage, load-shifting demand response reduces the ramping requirements by reshaping the load profile. In the real-time stage, reserve-capacity demand response releases the ramping capability by providing reserve for the rebalance of wind power fluctuation, which is represented by a data-driven uncertainty set. Since the dispatching scheme is formulated as a min–max-min problem, an accelerating solution method is developed to improve the computational efficiency, which combines the column-and-constraints generation algorithm and the specific property of the model. The simulation results tested on the modified IEEE 118-bus system and Henan power grid in China validate the effectiveness of the dispatching scheme and solution method in increasing flexible ramping capability, reducing wind power curtailment and load shedding, improving economic efficiency, and accelerating computation.

Two-Stage Stochastic Scheduling of Integrated Electricity and Natural Gas Systems Considering Ramping Costs With Power-to-Gas Storage and Wind Power

Power-to-gas (P2G) facilities and natural gas fired power units provide flexibility to integrated electricity and natural gas systems (IENGS) for wind power accommodation and ramp deployment. This paper proposes a stochastic coordinated scheduling model for IENGS considering ramping costs with P2G storage and wind power. The operation model of natural gas system with P2G is presented, and the benefits of P2G integration are analyzed. To address the uncertainties of wind power and energy loads, multiple representative scenarios are generated. The flexible ramping requirements and costs are incorporated and analyzed, and flexible ramp can be provided by P2G in this work. The coordinated scheduling model for IENGS is formulated as a two-stage stochastic programming problem, in which day-ahead scheduling for electricity systems is modeled in the first stage model and scheduling of natural gas systems is carried out in the second stage model. Numerical case studies on a modified PJM 5-bus electricity system with a 7-node natural gas system and the IEEE 118-bus system with a 20-node Belgian natural gas system verify that P2G can help accommodate wind power, provide additional flexible ramping capacities, and reduce the gas supply from gas suppliers and gas load shedding.

Suppression of Spur Chirps for Fractional-N PLL-Based Heterodyne FMCW SAR

Fractional-N phase-locked loops (PLLs) offer a low-cost solution to the precision frequency ramp requirements of frequency-modulated continuous-wave (FMCW) radar. Regrettably, however, these PLLs are known to produce undesirable spurii in their synthesized output. A subset of these spurii has recently been shown to manifest as frequency chirps, also known as spur chirps , in the deramped beat signal. These so-called spur chirps give rise to Fresnel ripples in the range domain after standard Fourier processing. Spur chirps are of particular concern for heterodyne receiver systems because the Fresnel ripples may appear at levels that far exceed the noise floor. In this article, the authors present guidelines for the avoidance of spur chirps and identify existing interference suppression techniques that are transferable to this problem. Furthermore, the authors put forward a novel technique termed range-Doppler spur masking (RDSM) that was designed specifically for the suppression of spur chirps. The developed technique was applied to both the simulated and measured synthetic aperture radar (SAR) data. Simulations reveal that RDSM significantly outperforms time-domain zeroing in terms of peak sidelobe ratio (PSLR) and other metrics. When applied to the measured data, RDSM is shown to eliminate the effects of the spur chirps in SAR imagery when masking only 0.15% of the available Doppler bins.

Dual-timescale generation scheduling with nondeterministic flexiramp including demand response and energy storage

Increased grid-connected wind power generation results in net load with higher volatility as well as larger ramping requirements. It is becoming imperative to provide flexible ramping products (flexiramp) for better generation / load alignment and smoother wind power integration. This paper advocates a non-deterministic flexiramp modeling approach devised in a hybrid stochastic / deterministic dual-timescale framework. The latter uses a day-ahead chance-constrained scheduling formulation along with a deterministic real-time online rolling scheduling model, to facilitate the procurement of flexiramp products in the two timescales. Demand response and energy storage are implemented and quantified as additional flexiramp product. Flexiramp reserve is introduced, to ensure sufficient flexiramp capacity throughout the timescales. To reduce the wear and tear of generators and consider the operational and maintenance costs in the proposed framework, cycling ramping (cycliramp) cost is subsumed as a penalty in the day-ahead objective function. Simulations are carried out for the 24-bus and 118-bus systems. Numerical results highlight the efficacy of the proposed generation scheduling models, along with the impact of contemplating flexiramp and cycliramp cost components in the dual-timescale scheduling approach.