Ramping Capacity Research Articles

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.

Optimal sizing and placement of battery energy storage system for maximum variable renewable energy penetration considering demand response flexibility: A case in Lombok power system, Indonesia

Indonesia is a tropical climate country with considerable renewable electrical energy source prospects, including photovoltaic (PV) and wind energies. Nevertheless, several variable renewable energy sources (VREs) have exhibited uncertain attributes and substantial reliance on natural conditions, leading to unstable load-related power supply risks. Hence, integrating battery energy storage systems (BESSs) with VRE generators is a dependable approach to bolster renewable energy generator applications on a large-scale grid while providing load demand flexibility. This study determined adequate sizing and placement of the BESS to achieve maximum VRE generator penetration while considering the demand response flexibility. Key indicators, including technical minimum load and system ramp capacity, were identified to achieve maximum penetration of thermal generators. This study also combined a unit commitment procedure with direct current optimal power flow (DC-OPF) as a novel approach to determine the maximum VRE penetration level. An optimization problem model concerning mixed integer linear programming (MILP) was subsequently employed in this study using the CPLEX solver in the general algebraic modeling system (GAMS). The study is based on the IEEE RTS-24 system modified and a real-life case study of the Lombok energy system in Indonesia. Results from the simulated Lombok power system highlighted that optimal sizing and placement of the BESS could lower system costs by 37.66%, 33.63%, and 22.26% compared to the current system conditions during the weekday, weekend, and the lowest day scenarios, respectively. The VRE penetrations of the generators were also higher than the current system conditions by 83%, 51%, and 39% during the weekday, weekend, and the lowest day scenarios, respectively.

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.

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.

Enhanced Flexible Ramping Product Formulation for Alleviating Capacity Shortage in Look-ahead Commitment

The roll-out of a flexible ramping product provides independent system operators (ISOs) with the ability to address the issues of ramping capacity shortage. ISOs procure flexible ramping capability by committing more generating units or reserving a certain amount of headrooms of committed units. In this paper, we raise the concern of the possibility that the procured flexible ramping capability cannot be deployed in real-time operations due to the unit shut-down in a look-ahead commitment (LAC) procedure. As a solution to the issues of ramping capacity shortage, we provide a modified ramping product formulation designed to improve the reliability and reduce the expected operating cost. The trajectories of start-up and shutdown processes are also considered in determining the ramping capability. A new optimization problem is formulated using mixed integer linear programming (MILP) to be readily applied to the practical power system operation. The performance of this proposed method is verified through simulations using a small-scale system and IEEE 118-bus system. The simulation results demonstrate that the proposed method can improve the generation scheduling by alleviating the ramping capacity shortages.

A Disposal Strategy for Tight Power Balance Considering Electric Vehicle Charging Station Providing Flexible Ramping Capacity

To tackle the high stochastic characteristic of renewable energy resources, such as wind power, sufficient flexibility is of great necessity to power systems. With increasing wind power penetration in the power system, the flexibility demand and the possibility of wind spillage are on the rise. Under the tight power balance state, the flexibility solely provided by thermal units will reduce the system flexibility, resulting in load shedding. It is imperative to utilize flexible resources to reduce wind spillage and load shedding. To address the above problems, the power system flexibility supply and demand model and electric vehicle (EV) charging stations operation model are first constructed in this paper. On this basis, a disposal strategy for tight power balance with EV charging stations providing flexible ramping capacity is established. Finally, numerical simulations are conducted on the modified IEEE 118-bus system. Results show that the integration of EV charging stations reduces the operation cost of the system by 11.72%, wind spillage by 17.88%, load shedding by 59.42% and provides flexibility to the system.

Robust flexibility driven security constrained unit commitment under wind uncertainty considering demand response and combined-cycle units

Operational flexibility aiming to mitigate variability impacts of Renewable Energy Resources (RES) inherently depends on inter-temporal operational status. Accordingly, in the current paper, a novel operational flexibility maximization procedure is developed within a robust Security Constrained Unit Commitment (SCUC) problem to find the most tolerable flexibility band in response to RES variability impacts. In addition, combined-cycle units (CCU) and incentive based demand response, from both generation and demand sides, are considered as flexibility providers because of their specific characteristics such as high ramping capacities and fast start-up/shut-down rates. However, unlike conventional units with single mode operation, a CCU unit is composedof multiple combustion turbines (CTs) and steamturbines (STs) that can operate in different modes. Since it is needed to decide the on/off statusand power output of each CT and ST unit at every timeinterval, the recourse decision layer will be non-convex. The challenging non-convex recourse decision level is cast as a mixed-integer program that is solved by an exact nested column-and-constraint generation algorithm. The capability of the proposed model is then investigated in standard IEEE 118-bus test system.

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.

Power System Robust Day-ahead Scheduling with the Presence of Fast-Response Resources Both on Generation and Demand Sides under High Penetration of Wind Generation Units

Increasing penetration of renewable energy resources has led to the need for operational flexibility of the power system to cover the uncertainty and variability of these resources. Therefore, employing flexible resources during the operation time will play an important role in nodal power balancing, providing the required ramping capacity, and reducing the operating costs of the power system. Some of the most important flexibility resources in the power system are fast-response resources such as gas and combined cycle units with the ability to provide non-spinning ramping capacity, bulk energy storage systems with the ability to switch from charge to discharge mode and vice versa in short-time period, and demand response programs with fast-response to price and incentive signals, which prevent the occurrence of severe ramps by correction of the system load factor. Therefore, contrary to the existing literature, this paper examines the impact of the above-mentioned fast-response resources on the IEEE 6-bus and IEEE RTS24-bus test systems both on the generation side and the demand side in form of the proposed model for day-ahead scheduling problem under high penetration of wind-based units. Another important point is the need to co-optimize energy and required ramping capacity reserve of the system with efficient accuracy and tractability. Thus, in this paper, a mixed-integer, two-stage and tri-level robust model with adaptive approach has been utilized, which is solved by nested column-and-constraint generation decomposition-based algorithm in order to jointly clear the energy and ramping capacity reserve with the presence of fast-response resources. The results show that utilizing the potential of fast-response resources with the aim of providing operational flexibility reduces operating costs by up to 10.61%.

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.

Capacity of Interchange in Outer Connection Ramps

Interchanges are constructed in urban and suburban areas to ease traffic operation. Outer connection ramps constitute a major part of interchanges planning and design. The objective of this study was to estimate interchange ramp capacity and investigate the impacts of ramp geometry on the obtained capacity. To achieve this objective, 20 ramps were selected, including 10 circular ramps and 10 curve- straight-curve ramps. Capacity of ramp proper and ramp exit were investigated in the study. At ramp proper, data on traffic speed and traffic flow were collected at 1-minute intervals using video camera. Also, traffic data for ramp exit and mainline traffic were obtained under queued traffic condition on the ramp exit. An empirical approach using regression analysis was adopted to estimate capacity of the selected ramps and explore the impact of geometric design and traffic variables that might affect the estimated capacity. For ramp proper, the analyses revealed that the relationship between traffic speed and density is linear and between speed and traffic flow is parabolic irrespective of ramp configuration. For circular ramps, the capacity was found to vary from 1470 to about 2100 pc/hr./lane, and it was found that ramp radius is the most influencing factor in capacity estimation. For curve-straight-curve ramps, the results indicated that the obtained capacity varied from 1490 to 2200 pc/hr./lane, and both the straight segment length and the radius of the first curve are the most influencing factors. Also, it was found that ramp exit capacity is affected by flow and speed of traffic in the mainline street and degree of curvature of ramp exit curve. Finally, it was concluded that the use of curves with large radii at ramp proper and exit would increase ramp capacity, and customization of capacity values to reflect local conditions would provide more realistic estimates.

Developing a Market-Oriented Approach for Supplying Flexibility Ramping Products in a Multimicrogrid Distribution System

Rapid growth of the variable and intermittent renewable generation in multimicrogrid distribution network (MMG-DN) has derived a gross violation in power balance due to the scarcity of ramp capacity. To neutralize this shortage, the present study develops a market-oriented approach for supplying flexibility ramping products (FRPs) in an MMG-DN. In this article, the proposed approach appraises a ramping capacity of local microgrids (MGs), which encompass various distributed energy resources through a bilevel programming. To do so, an interval-based security constraint economic dispatch process is accomplished at the upper level considering the network's requirement of upward/downward ramp capacity along with addressing the uncertainties. In addition, local MGs establish their optimal bidding strategies to supply FRPs at the lower level according to the offer prices from the distribution network. A direct mathematical technique has been utilized to convert the proposed bilevel programming into the single level one called mathematical program with equilibrium constraints (MPEC). Finally, the adopted nonlinear MPEC transforms into a mixed-integer linear programming using the strong duality theorem. The proposed approach has been examined on a test system. The results prove efficiency of the proposed framework in providing adequate FRPs for the MMG-DN without relying on the upstream grid.

Pricing Multi-Interval Dispatch Under Uncertainty Part II: Generalization and Performance

Pricing multi-interval economic dispatch of electric power under operational uncertainty is considered in this two-part paper. Part I investigates dispatch-following incentives for generators under the locational marginal pricing (LMP) and temporal locational marginal pricing (TLMP) policies. Extending the theoretical results developed in Part I, Part II evaluates a broader set of performance measures under a general network model. For networks with power flow constraints, TLMP is shown to have an energy-congestion-ramping price decomposition. Under the one-shot dispatch and pricing model, this decomposition leads to a nonnegative merchandising surplus equal to the sum of congestion and ramping surpluses. It is also shown that, comparing with LMP, TLMP imposes a penalty on generators with limited ramping capabilities, thus giving incentives for generators to reveal their ramping limits truthfully and improve their ramping capacities. Several benchmark pricing mechanisms are evaluated under the rolling-window dispatch and pricing models. The performance measures considered are the level of out-of-the-market uplifts, the revenue adequacy of the system operator, consumer payment, generator profit, level of discriminative payment, and price volatility.

Estimating abilities of distributed energy resources in providing flexible ramp products for active distribution networks

Growing demand for flexible ramp products (FRPs) to maintain the power balance in active distribution networks (ADNs) has expanded the use of costly fast-start generators in the upstream real-time market. In order to limit the power system’s reliance on these units, it is necessary to establish new sources for providing ADNs with FRPs. In this context, the present paper examines the abilities of distributed energy resources (DERs) and active loads for supplying the ramp capacity demands of ADNs. Accordingly, a local real-time market was established in the ADN, empowering several DERs and active loads to provide the network’s FRPs demands. The proposed market was formed in the context of a multi-period security constraint economic dispatch (SCED) problem. Besides, the uncertainties associated with renewable generation units and load consumption in the ADN were addressed by robust optimization. The proposed multi-period SCED problem was formulated linearly and in two stages. The first stage specifies the optimal dispatch results for the binding interval based on the network’s current conditions, whereas an adaptive robust SCED problem is handled in the second stage considering future periods. The local market developed within a two-stage robust multi-period SCED problem was then solved using a column-and-constraint generation method selected for its computational efficiency. Finally, the proposed market was studied on the modified 33-bus distribution network. The results indicated that DERs have sufficient capacity to support the ADN ramp capacity requirement.

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.

Enhancing electricity market flexibility by deploying ancillary services for flexible ramping product procurement

The escalated penetration of wind energy in the electric industry has by large contributed to an intensified uncertainty and variability of the net-load, i.e., the difference between stochastic demand and intermittent supply. In response, some Independent System Operators (ISOs) have recently introduced the Flexible Ramping Product (FRP) to heighten the grid flexibility via effective hosting of the uncertain renewables. This paper suggests a novel framework for optimal FRP provision in real-time markets (RTM) in which the shortage in the RTM ramp capacity is compensated by optimal deployment of day-ahead procured spinning reserve. In the envisioned mechanism, the ISO can deploy specific portions of the spinning reserve capacity as the FRP price increases. On this basis, a new optimization algorithm is formulated that can best capture the joint ability of ESSs and thermal generating units in providing FRP in the RTM. The proposed model is numerically investigated on the IEEE 118-bus test system, where it reveals its efficacy and applicability in load curtailment reduction.

Can electricity pricing leverage electric vehicles and battery storage to integrate high shares of solar photovoltaics?

Leveraging electric vehicles with controlled charging has the potential to advance the integration of high shares of residential solar photovoltaics. Time-varying electricity pricing is a promising tool to control EV charging indirectly through price signals, but also affects the diffusion and usage of other residential technologies. In this article, we develop an agent-based model to simulate California’s residential market for electric-vehicle charging, and the adoption of solar photovoltaics and battery storage, between 2005 and 2030. We show that time-of-use and hourly rates have a substantial impact on the further diffusion and integration of these technologies. Time-of-use rates trigger the adoption of battery storage, but over-coordinate electric-vehicle charging. Hourly rates, in contrast, slow down the diffusion of solar photovoltaics temporarily, but concentrate electric-vehicle charging around midday, thereby reducing the need for fast-ramping generation capacity and carbon emissions. Using real-world driving patterns, we show that 80% of EVs shift charging to midday hours with home charging alone. However, EVs only reduce the need for ramping capacity and thus advance PV integration, when users also have access to workplace and public charging. Further, we demonstrate that electric vehicles mitigate the increase in retail electricity prices, and thus counteract the utility death spiral. Our results indicate that controlling EV charging with electricity pricing decreases utility costs but increase retail electricity prices.

Quantifying accommodated domain of wind power for flexible look-ahead unit commitment

Properly scheduling flexible regulation resources plays a critical role in reliable operation of the power systems with high penetration of wind power. However, it remains an open question as to how to decide an operation guideline for this system to avoid the security violation and shortage of flexible ramping capacities (FRC). Amongst, it is crucial to determine the wind power injections that the system can accommodate by carefully considering FRC requirement from the whole scheduling interval. In this paper, the concept of accommodated domain of wind power (ADWP) is proposed as a metric for quantifying flexibility and an operational guideline. On this basis, two approaches for flexible look-ahead unit commitment (LAUC) are presented: Three-stage interval optimization approach, by which the largest ADWP is obtained and its corresponding operating cost is also optimized; and scenario-based stochastic optimization approach is used to minimize the overall cost, so as to obtain the most economical ADWP. Test results of IEEE 30-bus and IEEE 118-bus systems show that the proposed approaches for LAUC can effectively handle the ramping requirements and reduce the overall cost.

Electric Vehicle Participated Electricity Market Model Considering Flexible Ramping Product Provisions

This article studies electric vehicle (EV) potential to participate in the energy market and provide flexible ramping products (FRPs). EV traffic flows are predicted by the deep belief network, and the availability of flexible EVs is estimated based on the predicted EV traffic flows. Then, a novel market mechanism in distribution system is proposed to encourage the dispatchable EV demand to react to economic signals and provide ramping services. The designed market model is based on locational marginal pricing of energy and marginal pricing of FRPs. System ramping capacity constraints and EV operation constraints are incorporated in the proposed model to achieve the balance between the system social cost minimization and the EV traveling convenience. Moreover, typical uncertainties are considered by the scenario-based approach. Finally, simulations are conducted to verify the effectiveness of the established model and demonstrate the contributions of EVs to the system reliability and flexibility.

Development a new costing structure for palm oil industry using time driven activity based costing

Palm oil is one of the agricultural commodities most marketable in the world. The Malaysian palm oil industry is undoubtedly the pride of the country. There are few problems in this plantation at present, which do not represent a time frame to measure operations deviation, a rate establishment did not properly explain the connection between supplied assets and functional capacity and a method for tracking unused capacity was not available to the manager. This work aims at developing a new costing model specifically for ramp areas for improved precision. Time-driven activity-based costing (TDABC) is introduced because it can measure time output effectively, correctly identify idle energy and separately list the unused capacity. Costing based on time activity has been implemented. It also offers a better understanding of operating instruments and associated costs when assessing processes and promoting improvements in quality. There are three main divisions in the plantation in Pahang. The maximum utilized ramp capacity and the level of capacity cost rate (CCR) were successfully established with 4800 minutes and 1.00 RM / minute respectively. Finally, the manager finds the unused capability that can be used to improve ramp-resource management on a regular basis. However, the unused capacity created in ramp activities is not present in this case study.