Capacity Price Research Articles

A Mechanism Framework for Clearing Prices in Electricity Market Based on Trusted Capacity of Power Generation Resources

A reasonable capacity market mechanism is conducive to exploring the capacity value of different power generation resources and ensuring the adequacy of power supply capacity in power systems. In response to the challenges faced by the existing capacity market mechanism under the background of energy transformation, such as the unreasonable quantification of the support effect of different power generation resources on the power capacity of power system and the imperfect pricing mechanism of power capacity, a capacity market mechanism for power systems with high proportion renewable energy has been designed. To quickly clarify the capacity support effect of different power generation resources, a capacity credibility factor is introduced to quantify the actual contribution of different power generation resources in capacity supply and to deeply explore the capacity value of power generation resources. Based on the uniform marginal clearing price in the capacity market, the marginal clearing price of different power generation resources is corrected by using the cost ratio factor, which includes the difference in the cost structure of power generation resources. By comparing and analyzing examples, the proposed cost ratio factor can effectively optimize the capacity price; the maximum price difference is 18.2 yuan/MW, the overall capacity cost of the system is reduced by 53.70%, and the effective connection between fixed cost and variable cost of power generation resources is realized.

Coordinated planning of thermal power, wind power, and photovoltaic generator units considering capacity electricity price

AbstractWith the implementation of China's carbon reduction policies, the role of thermal power units will transition to a regulating power source. Hence, the electricity market fails to accurately reflect the capacity value of thermal power units, resulting in potential future losses for these units. Therefore, it is imperative to establish a rational capacity compensation mechanism that guarantees the revenue of thermal power units and offers effective investment and construction signals. Therefore, this paper proposes a capacity compensation mechanism for thermal power units based on effective capacity. To achieve this, a two‐layer power source planning model is established. At the upper level of the model, the installed capacity and capacity price of various types of power sources are optimized, while minimizing the operating costs of the planning horizon year under constraints such as annual net profit of units. The lower level focuses on the operation of typical days, optimizing the output of various types of units. Through case analysis, it can be concluded that the proposed model can achieve coordinated planning of capacity and capacity prices for various types of units, effectively ensuring the economic efficiency of the system while safeguarding the revenue of each unit.

The quality admission and price decisions of production capacity on the third‐party platform for shared manufacturing

AbstractMany third‐party platforms for shared manufacturing (platforms) have developed rapidly in recent years. The quality admission and pricing of production capacity are crucial issues for these platforms. This paper focuses on the platform supply chain composed of a platform, multiple manufacturers with surplus production capacity (sharers), and multiple manufacturers with insufficient production capacity (renters). Considering the impact of the production capacity quality on the shared scale and rental demand of production capacity, this paper constructs a game model for the platform supply chain under different quality admission scenarios, investigates the quality admission and price decisions of production capacity on the platform, and analyses the impact of the quality admission on the sharers’ and renters’ profits. The results show that the platform sets the quality admission when the sharers’ production capacity scale is small and the platform regulatory cost is low. The platform always increases its service price but may reduce the rental price of production capacity under the quality admission scenario. When the sharers’ production capacity scale is large, the platform lowers the threshold of quality admission with the quality elasticity of production capacity increasing. In addition, when the quality elasticity of production capacity and platform regulatory cost are low, and the sharers’ production capacity scale is small, the quality admission of the platform realizes the win–win–win situation for the platform, renters, and sharers.

Design an intermediary mobility-as-a-service (MaaS) platform using many-to-many stable matching framework

Mobility-as-a-service (MaaS) provides seamless door-to-door trips by integrating different transport modes. Although many MaaS platforms have emerged in recent years, most of them remain at a limited integration level. This study investigates the assignment and pricing problem for a MaaS platform as an intermediary in a multi-modal transportation network, which purchases capacity from service operators and sells multi-modal trips to travelers. The analysis framework of many-to-many stable matching is adopted to decompose the joint design problem and to derive the stability condition such that both operators and travelers are willing to participate in the MaaS system. To maximize the flexibility in route choice and remove boundaries between modes, we design an origin–destination pricing scheme for MaaS trips. On the supply side, we propose a wholesale purchase price for service capacity. Accordingly, the assignment problem is reformulated and solved as a bi-level program, where MaaS travelers make multi-modal trips to minimize their travel costs meanwhile interacting with non-MaaS travelers in the multi-modal transport system. We prove that, under the proposed pricing scheme, there always exists a stable outcome to the overall many-to-many matching problem. Further, given an optimal assignment and under some mild conditions, a unique optimal pricing scheme is ensured. Numerical experiments conducted on the extended Sioux Falls network also demonstrate that the proposed MaaS system could create a win-win-win situation—the MaaS platform is profitable and both traveler welfare and transit operator revenues increase from a baseline scenario without MaaS.

The Cost Reduction Potential of Demand Response in Balancing Markets from a System Perspective

Demand response (DR) can potentially provide a cost-efficient alternative for balancing the electricity grid by replacing fossil-fuelled power plants for the provision of flexible capacity. This paper aims to quantify the cost reduction potential of DR from a system perspective. Historical data of balancing markets are studied using regression and average bid price analysis to quantify the effect of the participation of DR resources on the price of flexible capacity for the provision of balancing reserves by focusing on two case studies in Great Britain and the Netherlands. It is estimated that DR bids are, on average, 35% lower than the market average. The regression analysis concluded that 1% higher participation of DR in balancing markets leads, on average, to a 2.7% lower prices for flexible capacity. The results verify the hypothesis that flexible DR capacity is offered at a lower price on balancing markets compared to conventional generation resources, resulting in lower costs for grid operators to balance the grid, thus reducing societal costs for electricity provision and overall emissions through the integration of low-carbon balancing resources.

Investment Strategy and Benefit Analysis of Power and Heat Hybrid Energy Storage in Industrial Parks Based on Energy Performance Contracting

To solve the problems of a single mode of energy supply and high energy cost in the park, the investment strategy of power and heat hybrid energy storage in the park based on contract energy management is proposed. Firstly, the concept of energy performance contracting (EPC) and the advantages and disadvantages of its main modes are analyzed, and the basic scheme of EPC for parks is proposed combined with the actual demand. Furthermore, the multiple energy storage model for power and heat storage in parks is established, which includes lithium batteries and heat storage tanks. Based on this, minimizing the annual operation cost of parks is taken as the optimization goal, and the capacity optimization model for power and heat storage is constructed, which considers the investment costs, operation and maintenance costs, purchased energy costs, peak-shaving subsidy, and environmental subsidy. Finally, an industrial park is selected as an example of EPC to verify the effectiveness of our proposed investment strategy. The results show that compared with the situation before the energy-saving renovation, the park can save 35.14 ten thousand CNY in annual cost expenses. When the unit power price of the lithium battery exceeds 3900 CNY/kW, the unit capacity price exceeds 5460 CNY/kWh, the unit power price of the heat storage tank (HST) exceeds 6000 CNY/kW, and the unit capacity price exceeds 1000 CNY/kWh, the configuration of the lithium battery and HST in the park is no longer the optimal choice to perform the energy-saving renovation.

Design and Impact of Grid Tariffs

We propose a novel grid tariff design proportional to grid load and analyze its performance in comparison to other grid tariff designs with regards to (i) effectiveness, (ii) efficiency, (iii) profitability of technologies and (iv) equity. In the case of a large share of automated loads, time-of-use tariffs and critical peak prices create problematic new rebound peaks. Direct load control and capacity prices can reduce grid load without rebound peaks but are less effective at reducing both grid and energy costs. The novel tariff design proportional to the grid load can reduce both grid and energy costs but needs to be designed appropriately to avoid rebound peaks. Tariff impacts on the profitability of different technologies are more pronounced than equity impacts because households from all income brackets may be equipped with PV and flexible technologies.

Forecasting capacity price in CA power market (CAISO)

Forecasting capacity price in CA power market (CAISO)

Fully Parallel Algorithm for Energy Storage Capacity Planning Under Joint Capacity and Energy Markets

Energy storage (ES), with its flexible characteristics, has been gaining attention in recent years. The ES planning problem is highly significant to establishing better utilization of ES in power systems, but different market regulations impact the ES planning strategy. Thus, this paper proposes a novel ES capacity planning model under the joint capacity and energy markets, which aims to minimize the total cost for power consumers. The great challenge is that the ES planning model has a large number of time periods, which significantly increases the problem dimensionality. In order to alleviate the computational burden, a fully parallel algorithm is proposed to temporally decompose the original problem into a series of small sub-problems, which can be solved in parallel. Moreover, we find that the corresponding analytical solutions to the sub-problems remarkably accelerate the calculation speed while ensuring accurate results. Finally, numerical results verify the effectiveness of the proposed model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Energy storage (ES) has become more and more essential to guaranteeing power balance in power and energy systems by shifting peak loads to valley loads. However, investors may face challenges to ES capacity planning due to the lack of business models. To address this challenge, price tariffs should be carefully investigated. In the practical power system, the market price should consider both the energy price and capacity price for industries and big companies. In the energy market, investors can gain a profit by selling energy at the peak load (high price) and buying energy in the valley (low price). It should be noted that the energy market cannot recover the ES investment cost, but investors can, in fact, reduce their capacity cost since ES can reduce the peak load. Consequently, we have designed a new business model for ES planning under joint capacity and energy markets to analyze the profits via the two market regulations. The computational burden is another challenge for the proposed multi-period convex optimization model. The model must consider a long-term simulation, potentially containing thousands of time periods, which can be difficult to solve. In order to alleviate the computational burden resulting from the long-term market simulation, we further propose a fully parallel algorithm to solve the proposed business model for ES quickly. To sum up, the proposed model and method have been tested on a practical company with a practical price tariff in China to show their effectiveness.

Research on the capacity cost allocation and the electricity capacity price optimization method for a power system based on the BARY‐GA algorithm

AbstractUnder the new power system, a high proportion of new energy is widely connected to the power grid, and it is necessary to increase investment in centralized and distributed energy storage, flexible resource regulation, and transmission and distribution grids, resulting in an increase in power system costs. The current electricity capacity price does not reflect the economic value of the added system adequately, and flexible capacity is needed to ensure the safety of the power grid under the new power system. In the context of the transformation of the new power system and investment function, it is increasingly difficult to recover the costs according to the traditional electricity price. It is necessary to establish a coordinated development mechanism for the electricity capacity price and the electricity price that recovers the fixed costs according to the investment function to ensure a reasonable return on investment in flexible resources and to provide effective investment signals. Therefore, this work first studied and proposed a mechanism for the formation of capacity and electricity prices based on the proportion of allowed income and fixed costs at different voltage levels and calculates the level of electricity capacity (demand) prices and electricity consumption prices. Then, using the BARY curve, genetic algorithm, and clustering of user load rates, a collaborative mechanism between the electricity capacity price and the electricity price was designed. With the goal of optimizing the electricity capacity price and considering constraints such as the flexibility and reliability of the new power system, the ratio of the capacity cost allocated to the electricity capacity price by voltage level users to the capacity cost allocated to the electricity price was calculated, and the optimal combination of electricity capacity price and electricity price for different load‐rate intervals was determined. Finally, the effectiveness of the model was verified through numerical simulations. On the basis of the investment characteristics of the new power system, suggestions for a capacity pricing mechanism under the new power system were proposed from the perspectives of transfer capacity function, transfer electricity function, grid reliability, and environmental and social benefits.

Optimized configuration and operation model and economic analysis of shared energy storage based on master-slave game considering load characteristics of PV communities

As a new form of energy storage, shared energy storage (SES) is characterized by flexible use and high utilization rate, and its application in photovoltaic (PV) communities has not yet been promoted because of the unclear operation mode and revenue effect. This paper focuses on the configuration, operation and economic benefits of SES in PV communities, comparing the differences in electricity consumption behavior and cost of electricity in PV communities with and without SES scenarios. First, a trading framework between SES and PV community is constructed by combining the reality of China's power market. SES provides charging and discharging services as well as dynamic capacity rental service, and PV community chooses the capacity as well as the time to use SES according to its own situation. Then, the decision-making advantage of SES is considered, and a SES-PV community model based on the master-slave game is established, where the upper level optimizes the system capacity configuration and operation with the goal of minimizing the cost of SES, and the lower level optimizes the electricity consumption behavior with the goal of minimizing the cost of electricity consumption in the PV community. Finally, the adopted model is simulated to obtain the optimal configuration capacity, operation strategy of SES and load optimization results for PV community. The respective costs of SES and PV community are further measured. The results show that SES is only profitable with subsidies, and the cost of electricity in the PV community with SES is reduced by 17.16 % compared to the community without SES. In addition, a sensitivity analysis is conducted to address the benefits of SES, which can be realized through the use of more cost-effective construction materials, access to the electricity market, appropriately increasing the price of dynamic rental capacity, and selecting the operation in the area where the PV output and the community load are more compatible. The research can provide reference for the development mode of SES and the optimization of energy use in PV communities.

Generation failures, strategic withholding, and capacity payments in the Turkish electricity market

Since the deregulation of significant parts of electricity markets in many countries worldwide, a generally pronounced matter is that firms strategically lower their generating capacity to raise electricity prices. This paper investigates whether strategic capacity withholding exists in the form of generation failures in the Turkish day-ahead market and whether the capacity remuneration mechanism contributes to the failure durations. The empirical results show strong support for strategic capacity withholding in the Turkish market, and the capacity mechanism adds to the duration of these failures. To the best of our knowledge, this is the first study to investigate capacity withholding considering centrally determined capacity prices and to focus on failure durations and the potential amount of power generation losses they cause. Our analysis conveys core messages for policymakers. A random verification mechanism may be implemented to verify that the failures are purely technical and apply penalties for nonperformance. Second, the Turkish capacity mechanism needs to be restructured to make it more compatible with performance incentives. Our analysis, while focused on the Turkish market, provides insights into the prevalent challenges and potential solutions tied to strategic capacity withholding in deregulated electricity markets globally, notably those employing capacity remuneration mechanisms.

Hybrid renewable energy systems: the value of storage as a function of PV-wind variability

As shares of variable renewable energy (VRE) on the electric grid increase, sources of grid flexibility will become increasingly important for maintaining the reliability and affordability of electricity supply. Lithium-ion battery energy storage has been identified as an important and cost-effective source of flexibility, both by itself and when coupled with VRE technologies like solar photovoltaics (PV) and wind. In this study, we explored the current and future value of utility-scale hybrid energy systems comprising PV, wind, and lithium-ion battery technologies (PV-wind-battery systems). Using a price-taker model with simulated hourly energy and capacity prices, we simulated the revenue-maximizing dispatch of a range of PV-wind-battery configurations across Texas, from the present through 2050. Holding PV capacity and point-of-interconnection capacity constant, we modeled configurations with varying wind-to-PV capacity ratios and battery-to-PV capacity ratios. We found that coupling PV, wind, and battery technologies allows for more effective utilization of interconnection capacity by increasing capacity factors to 60%–80%+ and capacity credits to close to 100%, depending on battery capacity. We also compared the energy and capacity values of PV-wind and PV-wind-battery systems to the corresponding stability coefficient metric, which describes the location-and configuration-specific complementarity of PV and wind resources. Our results show that the stability coefficient effectively predicts the configuration-location combinations in which a smaller battery component can provide comparable economic performance in a PV-wind-battery system (compared to a PV-battery system). These PV-wind-battery hybrids can help integrate more VRE by providing smoother, more predictable generation and greater flexibility.

A Market Framework for a 100% Renewable Energy Penetration Spot Market

The global installed capacity of renewable energy sources (RESs) is increasing rapidly. The 100% RESs power supply is being pursued. When renewable energy penetration tends to be 100%, the source-load coordination problem becomes extreme. The problems of market failure caused by the near-zero marginal cost of RESs and the scarcity pricing of flexible resources (FRs) participating in the rapid energy deviation balance need to be solved. Accordingly, a spot market framework including the day-ahead renewable energy temporal capacity market and the real-time energy sharing market is proposed in this paper. The RESs and FRs are decoupled to ensure that the homogenous commodities compete together. In the day-ahead stage, to obtain the locational marginal price with the guiding role of resource allocation, the fixed cost of the RESs is apportioned to obtain the spatial-temporal capacity price. In the real-time stage, a price-driven distributed energy sharing mechanism is proposed. It is proven that the sharing mechanism can achieve optimum social welfare and satisfy incentive compatibility. The simulation verifies that the new framework can solve market failure. The real value of FRs is discovered when a price consensus is reached.

Dynamic ordering and buyer selection policies when service affects future demand

Make-to-stock suppliers selling to regular buyers must balance the cost of overstocking against the cost of buyers’ reactions when items become unavailable. In selecting which buyers to satisfy when shortages occur, they must weigh the current revenue from the satisfied buyers against the loss in future demand from the dissatisfied buyers. To provide insight and decision support on these trade-offs, we (i) develop a novel newsvendor model of a firm with heterogeneous buyers with service-dependent demand, (ii) provide properties of the optimal ordering and buyer selection decisions of the firm, and (iii) derive a novel Lagrangian Relaxation (LR)-based index policy for selecting buyers and compare it with two other LR-based index policies. The model concerns a firm that orders items for a group of repeat buyers who generate different revenues and visit the firm with different average rates that depend on whether they are satisfied or dissatisfied with their last visit. The firm selects which buyers to serve if the demand exceeds the order quantity (current capacity). For two buyers, we show that a fixed order quantity (FOQ) policy and an index buyer selection policy are optimal. For more buyers, the optimal policy involves overstocking (understocking) when the overall buyer satisfaction is high (low) and selecting buyers that maximize the current revenue (future demand) when the overall buyer satisfaction after the demand is high (low). To tackle the problem, we derive three LR-based index policies: a Lagrangian index policy that uses a uniform capacity price, a Whittle index policy that uses a discriminatory capacity price and is myopically optimal, and a novel “active-constraint” index policy that uses a discriminatory capacity price when the capacity constraint is active. Numerical results indicate that the latter policy is near-optimal and outperforms the other two and that combining it with the right FOQ policy can be very efficient.

On the improvement of uncertain cloud service capacity

Cloud service firms often adopt a hybrid strategy in which computing capacity comes from a combination of their privately owned infrastructure and a public infrastructure provided by vendors. This combination can be considered a cloud computing supply chain. A major complexity for firms in managing such a supply chain is that computing demand and private computing capacity are both random variables. This randomness may require public cloud computing to fill the supply–demand gap and lead to additional costs. In this paper we study a cloud service firms’ problem of determining the amount of private computing capacity that minimizes their expected total capacity cost. We first demonstrate that the optimal amount of private computing capacity is unaffected by its randomness. We then establish that the reduction in expected total capacity cost, due to improvements in the mean or the variability of private computing capacity, exhibit diminishing returns, which allows characterization of parameter impacts and the optimal improvement level. We also establish that when setting their computing capacity price, public vendors do not need to know the private computing capacity of cloud service firms using a hybrid strategy. Our research results should be helpful to cloud service firms making procurement decisions regarding private computing capacity and/or instituting computing capacity improvement programs. Our research results should also be useful to public vendors for pricing.

Will Capacity Mechanisms Conflict with Carbon Pricing?

Climate change and related national mitigation targets make the decarbonization of the power sector an urgent need. The power sector faces the challenge of considering the design and interaction between emission reduction policies, which can sometimes counteract each other. This study proposes a framework that can be used to quantitatively study the qualitative link between carbon pricing and capacity pricing. The framework is validated through a case study in Hokkaido, Japan, and used to further investigate the interaction between the two policies through a System Dynamics simulation model and scenario design. The results indicate that a carbon price would promote the introduction of wind power, as well as the reduction in fossil fuels, while the capacity price will mitigate the boom-and-bust investment cycle and stabilize electricity prices. However, when the two policy-based prices act on the power system simultaneously, the advantages will be offset by each other. The existence of the capacity price partially offsets the emission reduction effect of the carbon price, and the carbon price with a lower floor will also indirectly squeeze the generation space of flexible power plants. In order to address these inefficiencies, this study proposed a capacity price focused on subsidizing flexible power plants and also coupled with a higher floor carbon price, which results in a consistent incentive. It also promotes the decommissioning of carbon-intensive base-load power plants and reduces CO2 emissions significantly.

Robust dispatching of integrated energy system considering economic operation domain and low carbon emission

The traditional robust optimization algorithm can only provide the scheduling plan in the worst case, but due to the time coupling constraints such as climbing and energy storage SOC(State of Charge) constraints, it is difficult to provide an adjustment scheme for intra-day scheduling plan. In the first stage, the upper and lower bounds of equipment output are taken as decision variables, and the cumulative change of load is introduced to deal with the constraint of demand response, so as to form the economic operation domain of the park. The Column-and-Constraint generation method (CCG) is used to solve the three-layer optimization problem, and the scheduling plan and optimal operating domain in the worst case are obtained. The intra-day scheduling can be adjusted arbitrarily according to the new energy output within the interval. Carbon transaction cost is included in the goal function to account for reduced carbon emissions. Simultaneously, to take use of the benefits of flexible adjustment, FM income is incorporated into the goal function, and the charging and discharging behavior of energy storage is defined by optimizing calculations based on real-time capacity prices during daily scheduling. The calculation results show that the proposed robust optimization algorithm based on economic operation domain can be regarded as an extension of the traditional robust algorithm. Besides, the total cost can be reduced and the economy can be improved by adjusting the scheduling scheme according to the new energy output in real time under the premise of ensuring the time coupling constraint.

Pricing model of reactive power services of shared energy storage considering baseline power for renewable energy accommodation

Driven by the gradual increase of the renewable energy installed capacity, shared energy storage (SES) has the potential to play an important role in the renewable energy accommodation scenario. In the process of accommodating renewable energy, SES users can also provide reactive power services to earn revenue and further improve the utilization rate of energy storage resources. In this context, a pricing model of reactive power services of SES is proposed in this paper to make SES users obtain reasonable revenue from the provided reactive power response capability. Firstly, based on the analysis of the cost composition, capacity constraint and application value of reactive power services, a cost-based time-variant capacity price is designed considering the investment cost and the opportunity cost of SES users. Moreover, the baseline power for renewable energy accommodation is proposed to quantify the opportunity cost caused by reactive power services in the renewable energy accommodation scenario. Finally, a case study based on the actual data of an SES trading pilot project in Qinghai, China, shows that the proposed pricing model can effectively reflect the costs of reactive power services which are highly coupled to the active power for renewable energy accommodation, and make SES users obtain the reasonable revenue covering the costs.

Purchase or rent? Optimal pricing for 3D printing capacity sharing platforms

Online sharing platforms have attracted considerable research and management attention across a number of industries, including travel, real estate, and cloud computing. They also have great potential for the 3D printing (3DP) industry, offering users the choice between owning or renting 3DP capacity. For matching supply and demand, capacity pricing is crucial. In this paper we consider two fundamental questions concerning pricing: (i) What is the optimal pricing strategy for a 3DP capacity sharing platform? (ii) How do usage level and printer heterogeneity affect consumers’ choice between in-house printing (owning) and outsourcing (renting)? Using queuing analysis, we derive the structural properties of the solutions to the problems. Furthermore, we conduct numerical studies using real-world data to generate managerial insights from the analytical findings. A key finding is that governments should focus on encouraging technological progress to lower the printers’ prices in order to improve the well-being of the industry. When considering two types of printers, we find that it is more beneficial for the platform if the high capacity printer dominates the market, as the platform then retains the prominent role in “redistributing” the 3DP capacity.