Reserve Market Research Articles

Market-Based Indicators on the Road to Ample Reserves

As noted in the excerpt above, the FOMC's Plans for Reducing the Size of the Federal Reserve's Balance Sheet envisioned a methodical approach to balance sheet normalization in which the passive runoff of securities holdings would stop when reserves levels are "somewhat above the level it judges to be consistent with ample reserves." Once balance sheet runoff stops, reserve balances would continue to decline with the growth of non-reserve liabilities for some time until they reach ample levels. The SOMA portfolio would then start growing with reserve management purchases in line with the growth in non-reserve liabilities. In light of lessons learned during the 2017-2019 balance sheet normalization, the Committee was mindful of the uncertainties surrounding estimates of the demand for reserves and sought to follow a process that would allow for careful monitoring of market conditions in determining when to "slow and then stop" balance sheet runoff.2The process of balance sheet runoff has proceeded very smoothly to date. The Federal Reserve's securities holdings have declined by nearly $2 trillion since the beginning of balance sheet runoff. As noted in a recent speech by Roberto Perli—the current manager of the System Open Market Account—a range of key metrics suggests that reserves remain abundant with few if any signs of significant market pressures of the type observed over 2018 to 2019.3Still, as Perli noted, the Federal Reserve continues to closely monitor a wide range of market indicators as well as information gleaned from surveys and contacts with market participants in assessing reserve market conditions during the transition to ample reserves.

Mobile base station site as a virtual power plant for grid stability

Energy grids and markets are in transition. Increased use of renewable energy sources (RES) introduces new stability challenges for power grids. Despite the substantial electrical consumption of mobile networks, they are yet to harness their inherent flexibility for aiding in the stability of the power grid. A noticeable research gap exists concerning measuring full activation time for fast frequency reserve (FFR) product while using batteries from mobile network base stations. Our objective is to demonstrate that mobile operators could use their existing infrastructure to participate in the reserve market of a contemporary power grid. Furthermore, it seeks to determine if the full activation time can meet the requirements of an FFR product. The system consists of a live mobile base station site with a mobile connection to the site, local controller, an existing battery, and a power system that, in combination, can function as part of a power grid balancing system. Our main finding indicates that the rectifier reaction time within an installed base station site infrastructure ranges from 5 to 8 s, and the time when the base station is entirely off from the grid varies from 7 to 10 s. This finding is significant since the activation time is too long for the base station power system controller to be used for FFR. The required full activation time for FFR is less than 1.3 s. In conclusion, power system vendors should investigate improvements for their equipment and software products to enable fast reserve market entry for their existing customers and stay competitive.

Stochastic Scheduling of Grid-Connected Smart Energy Hubs Participating in the Day-Ahead Energy, Reactive Power and Reserve Markets

An Energy Hub (EH) is able to manage several types of energy at the same time by aggregating resources, storage devices, and responsive loads. Therefore, it is expected that energy efficiency is high. Hence, the optimal operation for smart EHs in energy (gas, electrical, and thermal) networks is discussed in this study based on their contribution to reactive power, the energy market, and day-ahead reservations. This scheme is presented in a smart bi-level optimization. In the upper level, the equations of linearized optimal power flow are used to minimize energy losses in the presented energy networks. The lower level considers the maximization of profits of smart EHs in the mentioned markets; it is based on the EH operational model of resource, responsive load, and storage devices, as well as the formulation of the reserve and flexible constraints. This paper uses the “Karush–Kuhn–Tucker” method for single-level model extraction. An “unscented transformation technique” is then applied in order to model the uncertainties associated with energy price, renewable energy, load, and energy consumed in mobile storage. The participation of hubs in the mentioned markets to improve their economic status and the technical status of the networks, modeling of the flexibility of the hubs, and using the unscented transformation method to model uncertainties are the innovations of this article. Finally, the extracted numerical results indicate the proposed model’s potential to improve EHs’ economic and flexibility status and the energy network’s performance compared to their load flow studies. As a result, energy loss, voltage, and temperature drop as operation indices are improved by 14.5%, 48.2%, and 46.2% compared to the load flow studies, in the case of 100% EH flexibility and their optimal economic situation extraction.

Resilience-oriented scheduling of DC microgrid considering participation in the automatic frequency regulation reserves market

Maintaining uninterrupted supply to critical loads within a DC microgrid (DCμG) with minimal curtailment of flexible and moderate loads after an extreme event is challenging. In this context, this paper proposes a resilience-oriented energy management scheme (EMS) for an electricity-Hydrogen (H2) DCμG considering participation of the DCμG in the automatic frequency restoration reserve (aFRR) market. The DCμG works in grid-connected mode during normal operation and switches to island operation after an extreme event to minimize damage propagation. DCμG network and equipment-level operating constraints are formulated incorporating the effect of aFRR market participation and coordinated with the EMS scheme. The problem is formulated in the convex model predictive control (MPC) framework using a receding horizon, in which three operating periods, normal, proactive preparation, and emergency, are considered. Economical operation is achieved during normal operation, while during proactive preparation, the DCμG operator (DCμGO) economically prepares for the imminent extreme event to meet the criticality criterion of resilient operation. The emergency period starts after the extreme event, and the objective is to supply all critical loads with minimal curtailment of moderate and flexible demands. We have used a Copula-embedded Monte Carlo Simulation (MCS) framework for modelling the uncertainty of input random variable (RV)s and their correlations. Simulation studies validate the veracity of the proposed approach on a six-bus test DCμG system. Simulation results indicate that participation in the aFRR market increases the profit by ∼30.5%, and resilience-oriented scheduling reduces curtailment costs by ∼16.72% during emergency periods.

A Basic Study on an Electricity Market Model Integrating the Interaction between Spot and Reserve Market

A Basic Study on an Electricity Market Model Integrating the Interaction between Spot and Reserve Market

Two‐stage stochastic energy and reserve market clearing model considering real‐time offering strategy of renewable energy

AbstractWith the introduction of decarbonization policies worldwide, renewable energy, especially wind and solar power, is gradually taking a significant share in the power system. The two‐stage stochastic optimization methods are generally adopted to address the uncertainty issues and the two‐stage stochastic market clearing models are established with the participation of renewable energy. However, most existing two‐stage stochastic market clearing models usually intuitively presume the offering quantities of renewable energy are equal to their forecast values, neglecting the potential impacts of the strategic offerings on the market results and thus affecting the secure operation of power systems. To fill the gaps, this paper incorporates the strategic offering models of renewable energy into the two‐stage stochastic energy and reserve market clearing model. Within the two‐stage framework, the first day‐ahead stage serves for energy and reserve market clearing, while the second real‐time stage considers the adjustments on offering quantities of renewable energy according to the fluctuations of renewable generations. The two‐stage stochastic market clearing model is formulated as a mixed‐integer non‐linear programming problem, and an iterative algorithm is devised to obtain market clearing results after proper linearizations. The effectiveness of the proposed method is validated using the extended IEEE‐118 test system.

Powering the circular future: Climate change and economic perspectives on second‐life batteries in the Belgian context

AbstractThis paper calculates the future levelized cost of storage (LCOS) and conducts a prospective life cycle assessment (PLCA) for second‐life batteries (SLB) in Flanders, Belgium. A cradle‐to‐grave approach is chosen for climate change (CC) and economic impacts of SLB. Impacts of processes related to the first and the second life are allocated according to the delivered electricity. Furthermore, impacts are determined according to their temporal occurrence. For LCOS, activities are time adjusted by discounting. For PLCA, new background databases are generated and changed based on the activities occurrence in time. Additionally, future CC impact of three Belgian energy paths is modeled, introducing user‐defined scenarios of PLCA. To conceptualize impacts, three use cases are defined: (a) residential, (b) industrial and (c) utility use case (UTI). The residential and industrial use cases (INDs) represent photovoltaic (PV) installations with battery storage, the UTI is a large‐scale battery participating in the Belgian secondary reserve market. Lowest LCOS of the SLB in 2050 are found in the IND, namely 39.66 €/MWh, and are below the benchmark batteries. CC impact of SLB in the residential is 58.7 /kWh and below the benchmark batteries. The CC impact of SLB is 75.2 and 78.5 /kWh in the industrial and UTI and thus higher than the benchmark batteries. Crucial for both assessments are increased dismantling and repurposing facility throughput, fair charging tariffs, the manufacturing, the charging electricity generated by PV installations, and power electronics. On the contrary, changing the background does not lead to major changes in CC.

Optimal scheduling of virtual power plants with reversible solid oxide cells in the electricity market

Virtual power plants (VPP) can act as an independent entity to aggregate different distributed resources to participate in the electricity market, which is an inevitable trend in building green and low-carbon power systems. To investigate the beneficial effect of bi-directional electric-hydrogen conversion on market trading and optimal scheduling of VPP, this paper presents an optimal scheduling method for VPP with reversible solid oxide cells (RSOC) in the electricity market. Firstly, according to the Butler-Volmer equation and Faraday law, the relationship between the hydrogen flow rate and the electric power is established, and the equivalent physical models of the two RSOC working modes are developed based on the relationship. Then, the optimal scheduling model of the VPP participating in the energy and reserve joint market is proposed based on the two-stage robust optimization theory. The system operator reserve demand is used to describe the uncertainty of the transactions between VPP and reserve market. Finally, the column and constraint generation (C&CG) algorithm is used to solve the two-stage robust scheduling model. The simulation results show that RSOC can effectively promote renewable energy consumption and improve the VPP operating profit. Where, the operating profit can be increased by 46.5 % and the wind power consumption rate can be increased by 3.4 %, compared with the non-hydrogen VPP. Furthermore, compared with the traditional conversion equipment, RSOC can increase the VPP operating profit by 2.3 % and the wind power consumption rate by 1.85 %.

Electricity arbitrage for mobile energy storage in marginal pricing mechanism via bi-level programming

The rapid growth of renewable energy is integrated into the distribution system. The creation of both new market mechanisms and investment models has critical effects on the economics and security of the distribution market. Mobile energy storage has been used to increase the resilience of distribution grids due to their advantages in mobility and flexibility, which offer electricity arbitrage options for merchant investments. This paper presents a bi-level optimization framework based on location marginal pricing settlement of mobile energy storage financial rights revenue in active distribution systems. In the developed framework, the upper-level problem determines the optimal capacity, routing, and dispatching of mobile energy storage to maximize revenue in the liberalized electricity market. The lower-level problem performs the joint optimization of energy and reserve market clearing as well as renewable energy capacity optimization based on the alternating current optimal power flow model. The non-convexity of the line flow and location marginal pricing is linearized via second-order cone relaxation and Karush-Kuhn-Tucker. The bi-level programming is reformatted as mathematical programming with equilibrium constraints. Further, the revenue risk due to renewable energy uncertainty is measured via conditional value-at-risk. Finally, the effectiveness of the optimization framework and solution method is verified using the modified IEEE-33 test system. The results show that mobile energy storage promotes renewable energy and reduces distribution network costs by 2.3%.

Robust scheduling of a pulp and paper mill considering flexibility provision from steam power generation

The production scheduling and flexibility provision analysis are of great importance for industrial facilities to provide support for the main power grid. This study proposes a comprehensive scheduling model for an industrial pulp and paper (P&P) mill that considers the flexibility provision from steam power generation. In this model, the internal relationships between the production and consumption of electricity, steam, and pulp in the operational processes of the P&P mill are investigated. Meanwhile, the available flexibility of steam power generation is quantified and further enhanced for participation in the manual frequency restoration reserve (MFRR) market. Furthermore, a robust scheduling model that uses support vector clustering (SVC) technology is proposed to assess the impact of uncertainties in steam temperature and pressure on the available flexibility of steam power generation, thereby reducing the probability of failing to provide the contracted flexibility for MFRR. Simulation results demonstrate that the comprehensive scheduling model significantly increases the available flexibility of steam power generation for MFRR, resulting in a daily revenue increase of $1839.8. Moreover, a balance between the revenue obtained from the MFRR market and the probability of economic penalties incurred for failing to meet the contracted flexibility is achieved by comparing different levels of conservatism in the robust scheduling model.

Strategic investments in mobile and stationary energy storage for low-carbon power systems

The main feature and trend of the distribution system is the integration of renewable energy with high penetration rates. The variability and zero marginal cost characteristics of renewable energy will lead to drastically fluctuating locational marginal prices. In the deregulated electricity market, merchants have incentives to utilize energy storage and price arbitrage. Mobile energy storage has a short capital payback period and is widely recognized for transferring energy in the temporal and spatial dimensions. This paper analyses the interaction between merchants and distribution system operators and presents a hybrid energy storage strategic investment framework using bi-level programming. In the upper-level problem, the merchant formulates the capacity, location, and operation strategy of different energy storage to maximize the market revenue of hybrid energy storage, which is paid for by the locational marginal price. In the lower-level problem, the distribution system operator develops an optimal dispatch strategy considering renewable energy and merchant investments. A joint energy and reserve market clearing procedure is performed to derive the locational marginal prices. In the lower-level problem, a data-driven solution strategy based on machine learning is introduced. It uses deep neural networks to approximate the mapping relationship between distribution system states and locational marginal prices. The presented methodology is implemented in an IEEE-33 node system. The results demonstrate that, compared to the basic case, the hybrid energy storage investment strategy has led to an 8.1 % increase in merchant profits and a 12.9 % reduction in the operational costs of the distribution network. The deep neural network approximator fully avoids the lower-level problem with less than 5 % optimality loss.

Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets

Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets

Dynamic capacity withholding assessment of virtual power plants in local energy and reserve market

The increasing utilization of distributed generation resources led to the formation of active distribution networks and virtual power plants (VPPs), which have changed the paradigms of electrical energy transactions in local energy markets. The VPPs can form capacity-withholding groups and impose market power to gain more profits, which may increase the costs of energy procurements for consumers. This paper presents an algorithm for the local electricity market operator in distribution networks to assess the dynamic capacity withholding of VPPs in the local energy and reserve markets. The main contribution of this paper is proposing indices to evaluate the dynamic capacity withholding of VPPs in energy and reserve markets. The other contribution of this paper is that it also quantitatively analyzes the impact of withholding processes on the flexibility of the distribution network. An optimization process is used to estimate coordinated offers of VPPs in the energy market in order to prevent the formation of withholding groups. The proposed algorithm was assessed for the 123-bus IEEE test system and the energy and reserve dynamic capacity-withholding indices were determined for different operating conditions.

Cooperative V2G-enabled vehicle-to-vehicle sharing in energy and reserve markets: A coalitional approach

The dynamics of electric vehicles (EVs) charging significantly influence the current power system dynamics. However, with advancements in battery technology and charging infrastructure, EVs can also serve as energy storage systems through vehicle-to-grid (V2G) technology. This opens up possibilities for novel approaches, such as a coalition-based V2G-enabled vehicle-to-vehicle (V2V) energy and reserve sharing mechanism. Unlike traditional transactive energy models that often under-utilize EVs due to mismatches with smaller renewable outputs and peak loads, the proposed cooperative V2V sharing mechanism aims to maximize the use of EVs’ charging and discharging capabilities. It forms a grand coalition of EV users to optimize energy and reserve market participation. The model introduces mathematical formulations to describe how EVs collaborate in both energy and reserve markets. It ensures fairness and stability in pay allocations among users within the cooperative framework. The theoretical foundation includes proof of balance in the coalition approach and a two-stage imputation method to achieve fair and optimal payoff distribution. This minimizes incentives for users to defect from the coalition, ensuring stability. To address scalability challenges inherent in coalition formation problems, a decomposition algorithm is proposed. This algorithm enhances efficiency in solving problems that grow exponentially with the number of users. The effectiveness and superiority of this approach are validated through applications to various community energy systems of different sizes. The proposed plan can increase 22.21% of the total payoff in 10-users case and 22.39% in 30-users case. The computation time scales near-linearly with the number of users, although the computation scales exponentially with it. These demonstrations highlight its capability in modeling and solving complex energy sharing scenarios.

Role of power-to-heat and thermal energy storage in decarbonization of district heating

The sector integration of the heat and electricity sectors promotes the flexibility of the energy system. This, together with the already almost carbon dioxide-free electricity supply in Finland, facilitates the environmental transition to a carbon-neutral heating system. In this study, pathways to decarbonize the district heating system in Åland (an autonomous region of Finland) are explored. Especially, the roles of different power-to-heat technologies, e.g., heat pumps and direct electric heating, and thermal energy storage are investigated in the decarbonization of the district heating system. The focus is on studying the impact of varying prices in electricity markets on the operation of climate-neutral district heating systems. For this, three scenarios were modeled over the five years of 2018–2022 with varying demand and electricity market prices by using the modeling software PLEXOS. The study showed that electricity prices, but also reserve market prices, have a very significant impact on the operation cost and profit of a district heating system. Overall, electrification has the strong potential to reduce emissions cost-effectively in a district heating system, but it also makes the system vulnerable to electricity price volatility. It can be concluded that thermal energy storage and reserve market participation are crucial for protecting against cost increases and reducing investment risk.

Exploring optimal market operations and grid effects in an office building energy community: A case study

In this paper, we study optimal market operations and grid effects of office building from an energy community perspective. Commercial buildings such as offices can include many stakeholders and new flexible assets that can offer suitable use cases to form energy communities or enter energy and reserve markets. The office building we focus in this study has a real-world counterpart with historical measurement data and involves three stakeholders and their assets. The stakeholders are the owner, the tenant, and the operator of flexible assets. These assets are the battery energy storage system (BESS) and the electric vehicles (EV) charging system (EVCS) that both can operate in response to energy price fluctuations and in a reserve market. Different cases are studied where either the owner or the tenant is paying the EV charging, and where the flexibility of the EV charging is either active or not. We show that, when the owner pays for the energy of the EVCS and the tenant is using it, there is a beneficial case to form a coalition between them and share the benefits. Operative cost-benefit calculations are conducted where the community benefits are shared according to the Shapley value and the power flows at the building’s connection point are simulated based on the market operations. The sensitivity of the flexibility and the maximum power of EVCS to the results are studied. Harnessing benefits from the markets shows significant effects on the power flows at the connection point.

A peer‐to‐peer joint energy and reserve market considering renewable generation uncertainty: A generalized Nash equilibrium approach

AbstractPeer‐to‐peer (P2P) energy trading enhances distribution network resilience by reducing energy demand from central power plants and enabling distributed energy resources to support critical loads after extreme events. However, adequate reserves from main grids are still required to ensure real‐time energy balance in distribution networks due to the uncertainty in renewable generation. This paper introduces a novel two‐stage joint energy and reserve market for prosumers, wherein local flexible resources are fully utilized to manage renewable generation uncertainty. In contrast to cooperative optimization methods, the interactions between prosumers are modelled as a generalized Nash game (GNG), considering that prosumers are self‐interested and should follow distribution network constraints. Then, linear decision rules are employed to ensure a feasible market equilibrium and develop a privacy‐preserving algorithm to guide prosumers toward the market equilibrium with a proven convergence. Finally, the numerical study on a modified IEEE 33‐power system demonstrates that the designed market effectively manages renewable generation uncertainty, and that the algorithm converges to the market equilibrium.

Cost minimization of a hybrid PV-to-methanol plant through participation in reserve markets: A Danish case study

In efforts to combat carbon emissions in challenging sectors like heavy industry and shipping, Power-to-X (PtX) technology emerges as a promising solution. PtX involves converting renewable energy into hydrogen via water electrolysis and further processing it into eco-friendly fuels like methanol, ammonia, or kerosene. Denmark has seen a rise in plans for integrating large-scale PtX projects, which will introduce large electrical loads to its power system.To optimize the cost-effectiveness of PtX-based fuel production, a proposal suggests leveraging PtX plants’ inherent flexibility to provide frequency reserves to the electrical grid. The study centers on a real-life hybrid renewable energy system (HRES) dedicated to methanol production in Denmark.Analyzing the HRES technology and current reserve markets reveals that the HRES qualifies for frequency reserve provision certification, encompassing FCR, aFRR, and mFRR, by the transmission system operator. Through participation in the reserve market, the HRES can generate added revenue, resulting in substantial reductions of 12% and 30% in the levelised cost of methanol for 2020 and 2021 operational expenditure outcomes, respectively.The study illustrates significant cost reductions achievable through engagement in reserve markets, and these reductions may escalate further with increased market prices and volatility. Participating in frequency reserve capacity markets not only facilitates overall cost reduction but also serves as an insurance policy against unpredictable energy market fluctuations. This approach presents a promising trajectory to promote PtX technology adoption and nurture sustainable energy practices in challenging sectors.

Stochastic economic model predictive control for renewable energy and ancillary services trading with storage

The provision of renewable-based ancillary services (AS) is paramount for the stable operation of power systems featuring high renewable penetration. The combined operation of storage with renewables enables aggregators to increase the reliability of their energy and frequency control AS offers. Existing dispatch strategies for the supply of both energy and AS are usually rule-based or involve tracking a technical reference signal, hence economically suboptimal for aggregators. This study proposes a comprehensive decision framework in which first a stochastic optimization derives bids on energy and AS markets, then stochastic economic model predictive control (SEMPC) optimizes the storage dispatch in order to maximize the profit and minimize the storage degradation, as a function of the predicted renewable production and the expected AS activation. The framework is applied to a real-world case study where storage combined with wind power participates in the energy market, the frequency containment market and the frequency restoration reserve market. The SEMPC-based approach increases market revenue by 15% compared to a standard reference-tracking MPC, and reduces storage degradation by 23%. The stochastic formulation lowers the sensitivity of the economic objectives to renewable energy forecast errors, compared to deterministic approaches.

Exploring electric vehicle's potential as capacity reservation through V2G operation to compensate load deviation in distribution systems

This paper addresses the significant challenges faced by distribution system operators (DSOs) in competitive electricity markets due to uncertainties arising from electricity markets, electric vehicles (EVs), and conventional loads. To tackle these challenges, this paper proposes a pseudo-local reserve market between the DSO and EVs, which enables the DSO to integrate EV flexibility into the overall market framework while compensating for deviations in the conventional load across the wholesale electricity market. Furthermore, a two-stage framework is developed to facilitate the participation of EVs in electricity markets. In the first stage, the reserve capacity of EVs is integrated into the decision-making process for day-ahead bidding, taking into account uncertainties of the load and electricity. In the second stage, the day-ahead results are coordinated to enable the efficient dispatch of EVs for real-time market participation. To assess the efficacy of the proposed framework, a case study is conducted within a 31-node distribution system situated on a university campus located in Guangdong, China. This paper investigates the economic impact of load deviation constraints on EV reserves, revealing a penalty reduction of 3100 CNY for a daily reservation of 1000 EVs and a net profit increase of 1300 CNY for DSOs utilizing reserves. It further introduces a method for quantifying reserve capacity, enhancing DSO decision-making accuracy.