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A systematic solution to quantify economic values of vehicle grid integration
We report Vehicle-Grid-Integration (VGI) supplies one of the potential benefit extensions for electric vehicles (EVs) to make use of their parking time, which enables the EVs to provide grid services while still meeting consumer driving needs. However, the costs, benefits and risks of VGI still remain unclear, which limits the development of the VGI to promote the interaction between the EV and grid. In this study, we propose an integrated framework to quantify and utilize the aggregate flexibility of the EVs to supply the grid services in electricity markets. The integrated solution includes five sub-modules that cover end-to-end functionalities from individual EV energy consumption estimation to final monetary values calculation of providing grid services. Both wholesale market and local level charging management are formulated in the optimization module. A predictive control algorithm is proposed to allocate power to individual vehicles in real time, considering uncertainties from dispatch signal and travel behavior. Simulation results from 10,000 EVs indicate that the proposed optimization methods can significantly reduce the system cost in both wholesale market and retail market. Local tariff optimization reduces the electricity cost by 24.4% compared to uncontrolled charging. Wholesale market optimization results show that $\$$691 and $\$$255 revenues can be captured by each EV in ERCOT and CAISO markets per year, although with a conservative assumption on battery throughput cost at 0.16$\$$/kWh.
High-temperature Pumping of Silicon for Thermal Energy Grid Storage
As the cost of renewable energy falls below fossil fuels, the key barrier to widespread sustainable electricity has become availability on demand. Energy storage can enable dispatchable renewables, but only with drastic cost reductions compared to current batteries. One electricity storage concept that could enable these cost reductions stores electricity as sensible heat in an extremely hot liquid (>2000 °C) and uses multi-junction photovoltaics (MPV) as a heat engine to convert it back to electricity on demand, hours or days later. This paper follows previously reported technoeconomics and liquid containment, examining equipment that would be needed to exchange heat between resistive heaters, a molten silicon storage tank above 2000 °C, and a heat engine. Herein, we report on a pump that was designed and tested to circulate the liquid silicon between these three regions and the effect of spatial thermal cycling was simulated in models and experiments. While the pump successfully circulated silicon between 1800-2080 °C for ten hours, circulation with a temperature gradient caused it and other non-isothermal experiments to dissolve significantly due to the temperature dependent solubility of not only carbon, but also silicon carbide which otherwise protected the graphite infrastructure. Furthermore, methods to reduce dissolution and an alternative embodiment are presented.
A techno-economic assessment framework for hydrogen energy storage toward multiple energy delivery pathways and grid services
Hydrogen energy storage (HES) transforms and stores electric energy from the grid into hydrogen, and supplements other energy storage and demand response resources in addressing challenges in renewable-intensive power systems. Understanding how to optimally utilize an HES system to maximize its economic benefits from stacked value streams is highly important to its development and deployment. Here, in this paper, we present a techno-economic assessment framework for an HES system considering three common energy delivery pathways and multiple grid and end-user services. Models are developed to capture the operational capability, flexibility, and constraints associated with hydrogen production, compression, storage, and utilization as well as different grid services in an economic assessment. To define the technically achievable benefits, an optimal dispatch formulation is proposed to maximize the economic benefits over a representative year with an hourly time step considering the trade-offs among different value streams. Representative case studies are designed and carried out to show how system configuration, energy delivery pathways, and grid services may affect economic benefits. It was found that value streams from bundling grid services account for up to 76% of the total benefits and are critical for an HES project to be financially viable.
Proactive demand-side participation: Centralized versus transactive demand-Supply coordination
The active participation of demand-side flexible resources in the wholesale market price formation and load dispatch process is crucial to encouraging demand-side participation. This calls for a joint supply-demand coordination mechanism to fully take advantage of the flexible resources in distribution systems, including distributed energy resources (DERs) and responsive loads (RLs). Here this paper aims at comparing and evaluating the centralized and transactive distribution-level market coordination mechanisms. We introduce the centralized and transactive demand-supply coordination mechanisms for the distribution-level market and elaborate on the structural difference between the two frameworks. Relevant metrics and test scenarios are proposed for a meaningful comparison. The key observations of the comparative study are generalized from the perspective of different entities in the market: fixed loads, flexible loads, DERs, and conventional generators. It is observed that while the centralized approach leads to socially optimum solutions, the transactive approach by allowing for competitive bidding at the distribution-level, results in clearing higher flexible demand, and thus higher electricity cost at the transmission-level. As a result, DERs and fixed loads receive a higher surplus in the centralized approach, while conventional generators and flexible loads are more profitable in the transactive approach.
Enhanced Oblique Decision Tree Enabled Policy Extraction for Deep Reinforcement Learning in Power System Emergency Control
Deep reinforcement learning (DRL) algorithms have successfully solved many challenging problems in various power system control scenarios. However, their decision-making process is usually regarded as black-boxes. Furthermore, how DRL models interact with human intelligence remains an open problem. Thus, this paper proposes a policy extraction framework to extract a complex DRL model into an explainable policy. This framework includes three parts: 1) DRL training and data generation. We train an agent for a specific control task and generate data, which contains the control policy of the agent. 2) Policy extraction. We propose an information gain rate based weighted oblique decision tree (IGR-WODT) for DRL policy extraction. 3) Policy evaluation. We define three metrics to evaluate the performance of the proposed approach. A case study for the under-voltage load shedding problem shows that the IGR-WODT presents a performance enhancement compared with DRL, weighted oblique decision tree, and univariate decision tree. The proposed policy extraction method could provide an intuitive explanation of the neural network decision-making process to the dispatchers when making final decisions on power grid operation. Also, the resulted rule-based controller could replace the deep neural network-based controller in many field edge devices with limited computing resources, providing comparable performance.
Design analysis of a particle-based thermal energy storage system for concentrating solar power or grid energy storage
Energy storage is becoming indispensable for expanding renewable energy integration, and it is critical to the future low-carbon energy supply. Large-capacity, grid scale energy storage can support the integration of solar and wind power and support grid resilience with the diminishing capacity of baseload fossil power plants. With the development of thermal energy storage (TES) for concentrating solar power systems, standalone TES for grid integration becomes attractive due to the declining renewable generation cost and an increasing need for energy storage. The standalone TES system introduced in this paper can play a big role in the carbon-free energy future with capacity larger than batteries and cost likely lower than other energy storage methods such as pumped storage hydropower and compressed air energy storage, both of which also have geological limitations. To this end, we describe a TES system that uses stable, inexpensive solid particles as a TES media to provide scalable, low cost energy storage. The particle-based TES has the ability to drive various thermal power cycles including conventional steam-Rankine, air Brayton turbine with combined-cycle ability, or the emerging supercritical carbon dioxide Brayton power cycle. This work describes the containment design method including a concrete silo and an internal-insulation layer for the particle-TES system. The economic analysis shows significantly low storage cost when the particle-TES is integrated with Brayton combined-cycle power generation. The paper shows the design approach of the particle-TES system and its economic potential for bulk energy storage. The advantage of the particle-TES system as a promising bulk energy storage method is its ability to economically support dispatchable renewable grid penetration for larger capacity and longer discharging hours than current battery storage technologies.
A hierarchical framework for aggregating grid-interactive buildings with thermal and battery energy storage
The behind-the-meter (BTM) thermal and battery energy storage can help improve energy efficiency, reduce energy costs, and enhance energy resilience, particularly in rural areas and for disadvantaged communities. Aggregating numerous BTM energy storage systems can act as a price influencer with a significant source of load shifting and peak demand reduction. An integrated and scalable control mechanism is required to effectively utilize energy storage systems and flexible building loads to maximize the economic benefits, considering various distribution system constraints. Here, this paper presents an innovative hierarchical coordination framework for energy storage and flexible load in buildings, considering various factors such as electricity prices, thermal comfort, and distribution system modeling and constraints. At the upper level, a distribution system operator optimizes the power flow to minimize its power procurement costs from the electricity wholesale market, while at the lower level, aggregators determine the optimal dispatch of battery and thermal energy storage systems in multiple buildings on behalf of end-users to minimize operating costs according to the power prices. These problems are solved using a game-theoretic approach through negotiations between the distribution system operator and aggregators as a bi-level decision model. Simulation case studies have been performed for a test distribution network with a number of building end-users using energy storage systems to quantify the performance of aggregators. The results demonstrate that the proposed strategy can reduce peak load for a reliable electricity distribution network while saving electricity bills for customers.
Network reconfiguration and distributed energy resource scheduling for improved distribution system resilience
Electric utility companies work to restore as much load as possible after power outages caused by extreme weather events. In this paper, an outage management strategy is proposed to enhance distribution system resilience through network reconfiguration and distributed energy resources (DERs) scheduling. After a line fault, the proposed algorithm can identify radial network topology based on the rank of the incidence matrix. The reconfiguration is implemented by switching tie lines and sectionalizing lines. With the new network topology, an optimal DER scheduling problem is solved to minimize the accumulative cost for dispatchable DER operation and load reduction. Finally, the optimal topology that minimizes the accumulative cost is selected from all radial topologies. The computational workload is relatively low because only linear programming needs to be solved. Using the case studies of the IEEE 69-bus and IEEE 123-bus systems, we consider the worst-case scenarios in which faults occur in the upstream feeder. The simulation results demonstrate that the proposed strategy allows for a relatively high percentage of the load to remain in service after line faults. Furthermore, compared with microgrid-formation approaches, the proposed strategy has advantages when applied to the distribution systems with several normally-open tie lines and low DER penetration.
MPC-based double-layer real-time conditional cSelf-restoration for interconnected microgrids
In this report, we propose a novel model predictive control(MPC)-based real-time conditional self-restoration energy management system (CSR-EMS) for interconnected microgrids (IMGs) integrated with renewable energy sources (RESs) and energy storage systems (ESSs). Superior to the existing IMG self-restoration methods, the “conditionality” of the proposed CSR-EMS can economically realize self-restoration and grid-assisted restoration during energy deficiency or faults, in both islanded and grid-connected modes. Cost minimization is implemented as the objective function to judge in real-time which restoration mode is economically preferred. The proposed CSR-EMS comprises two layers–the lower layer operates locally to eliminate electricity fluctuations created by RESs and ensure economic effectiveness within an MG, whereas the upper layer oversees the real-time operational status of the IMG system and determines power exchange among microgrids (MGs) during abnormalities. In detail, when a microgrid inside the IMG system experiences an energy deficiency, the CSR-EMS, on an MPC basis, intelligently optimizes power production from each dispatchable distributed generator (DG), ESS, power imported from the main grid, and power exchange among the IMGs to maintain the demand–supply balance, while considering system recovery cost, state of charge (SoC) of ESSs and operation modes of the IMGs (i.e., grid-connected or islanded mode). Simulation results and comparisons with existing IMG self-healing EMSs demonstrate the economic efficacy of the proposed CSR-EMS strategy during normal and abnormal operations, which can be used as an energy control framework for modern power systems with multiple interconnected microgrids.
Leveraging automotive fuel cells can supply zero-emission peak power in the near-term
An increasingly decarbonized yet resilient power grid requires the corresponding build-out of dispatchable zero-emission resources to supply peak power. However, there is a recognized dearth of solutions which can serve multi-day peak demand events both cost-effectively and with near-term deployability. Here, we find that pairing low-cost automotive fuel cells with hydrogen storage in salt caverns can serve as a peaker plant at less than 500 US$/kW at present, a fraction of the cost of conventional fossil fuel-fired peakers. We demonstrate the peaker’s value for long duration storage by comparing it with pumped hydro and assessing its profitability within Texas’ energy-only market region. Although deployment of these peakers is constrained by the presence of salt caverns, we show that a number of sites in the United States and Europe are endowed with suitable salt formations, while utilizing hydrogen storage in pressurized containers could form a location-agnostic peak power solution.
Coordinated operation of electricity and natural gas systems from day-ahead to real-time markets
Power systems worldwide are becoming more reliant on energy from natural gas, wind, and solar, posing possible reliability and coordination challenges from the tighter coupling of these infrastructure systems. This paper proposes a framework for the market-based coordination of electricity and natural gas system operations. The proposed framework includes a power system model that accounts for flexibility in the commitment of power plants with short start-up and shut-down times, coupled with a dynamic gas model that simulates when gas cannot be delivered to generators. The capabilities of the framework are illustrated using real-world electric power and gas systems, including scenarios around wind and solar penetrations and the analysis of time-variant, “shaped flow” gas nominations. Our results indicate that coordination between power and gas systems improves total gas delivery and reduces out-of-merit order dispatch in the electricity system, and that shaped flows may reduce unserved gas in systems with high penetrations of wind and solar. Coordination can have mixed effects on carbon-dioxide emissions, with emissions increasing with coordination for current systems during high load weeks but decreasing for systems with high renewable penetrations, particularly during periods of high variability.
US building energy efficiency and flexibility as an electric grid resource
Buildings consume 75% of U.S. electricity; therefore, improving the efficiency and flexibility of building operations could increase the reliability and resilience of the rapidly-changing electricity system. We estimate the technical potential near- and long-term impacts of best available building efficiency and flexibility measures on annual electricity use and hourly demand across the contiguous U.S. Co-deployment of building efficiency and flexibility avoids up to 742 TWh of annual electricity use and 181 GW of daily net peak load in 2030, rising to 800 TWh and 208 GW by 2050; at least 59 GW and 69 GW of the peak reductions are dispatchable. Implementing efficiency measures alongside flexibility measures reduces the potential for off-peak load increases, underscoring limitations on load shifting in efficient buildings. Overall, however, we find a substantial building-grid resource that could reduce future fossil-fired generation needs while also reducing dependence on energy storage with increasing variable renewable energy penetration.
Economic evaluation of variable renewable energy participation in U.S. ancillary services markets
Variable renewable energy (VRE) is not yet meaningfully participating in U.S. ancillary services (AS) markets. VRE participation in AS markets could provide a new source of revenue for VRE resource owners to offset declining energy and capacity values and a new tool for power system operators to address emerging system constraints. This paper uses a price-taker dispatch model and historical prices to estimate the economic value of standalone and hybrid (battery-paired) VRE participation in AS markets, from the resource owner and electricity system perspectives, in each of the seven U.S. independent system operator and regional transmission organization (ISO/RTO) markets where ancillary service prices are set. Across ISO/RTO markets, average (2015–2019) simulated incremental revenues from power regulation market participation were 0.0–2.9 USD/MWh (+0–15% of revenue without participation) for standalone VRE owners and 1–33 USD/MWh (+1–69%) for hybrid VRE owners. However, ISO/RTO reserve markets are relatively thin and have the potential to become saturated by energy storage projects that are currently in ISO/RTO interconnection queues. In most markets, standalone and hybrid VRE could provide power regulation reserves during periods with high power regulation prices, suggesting that VRE participation in AS markets could have high system value. The analysis highlights the relevance of separate upward and downward power regulation products and indicates that ISOs/RTOs might consider initially focusing on enabling hybrid VRE provision of AS.
Air-stable, earth-abundant molten chlorides and corrosion-resistant containment for chemically-robust, high-temperature thermal energy storage for concentrated solar power
A dramatic reduction in man-made CO 2 emissions could be achieved if the cost of electricity generated from concentrated solar power (CSP) plants could become competitive with fossil-fuel-derived electricity. The solar heat-to-electricity conversion efficiency of CSP plants may be significantly increased (and the associated electricity cost decreased) by operating CSP turbines with inlet temperatures ≥750 °C instead of ≤550 °C, and by using thermal energy storage (TES) at ≥750 °C to allow for rapidly dispatchable and/or continuous electricity production. Unfortunately, earth-abundant MgCl 2 -KCl-based liquids currently being considered as low-cost media for large-scale, high-temperature TES are susceptible to oxidation in ambient air, with associated undesired changes in liquid composition and enhanced corrosion of metal alloys in pipes and tanks containing such liquids. In this paper, alternative high-temperature, earth-abundant molten chlorides that are resistant to oxidation in ambient air are identified via thermodynamic calculations. Here, the oxidation resistance, and corrosion-resistant containment, of such molten chlorides at 750 °C are then demonstrated. Such an air-tolerant strategy, involving chemically-robust, low-cost TES media paired with effective containment materials, provides a critical advance towards the higher-temperature operation of, and lower-cost electricity generation from, CSP plants.
High-efficiency, air-stable manganese–iron oxide nanoparticle-pigmented solar selective absorber coatings toward concentrating solar power systems operating at 750 °C
Solar selective absorber coating with long-term thermal stability at high temperatures ≥750 °C in air is an important component to reduce the levelized cost of energy (LCOE) of concentrating solar power (CSP) systems toward 50% power efficiency and dispatchable solar electricity. Conventionally, solar spectral selectivity requires multilayer-interference coatings implemented by stringently controlled vacuum deposition, and these coatings degrade significantly at >700 °C in air. Herein, we established a quantitative design approach and demonstrated a proof-of-concept air-stable, manganese–iron oxide nanoparticle (NP)-pigmented solar selective coatings with a high solar absorptance of ~93%, a relatively low thermal emittance of ~52%, and an optical-to-thermal energy conversion efficiency >89% under 1,000× solar concentration at 750 °C toward Generation 3 CSP systems. These coatings demonstrate spectral selectivity using cost-effective spray coating approach, a notable improvement over conventional vacuum-deposited, multilayer solar selective coatings for low-cost, high-efficiency solar thermal receivers. In contrast to the thermal degradation of spectrally non-selective benchmark Pyromark 2500 coatings at 750 °C in air, the solar absorptance of the MnFe 2 O 4 -pigmented coatings on stainless steel 310 (SS 310) substrates is increased to ~92.9% and the optical-to-thermal energy conversion efficiency is improved to 89.7% after serving at 750 °C in air for 700 h. X-ray diffraction results reveal that this improvement is due to the transformation of MnFe 2 O 4 NPs into more thermodynamically stable, non-stoichiometric manganese-rich manganese ferrite and iron-rich manganese–iron oxide phases after 500 h aging at 750 °C. For >1,000 h-endurance testing at 750 °C in air and the subsequent 19 day-night thermal cycling between 750 °C (12 h/cycle) and 25 °C (12 h/cycle) on SS 310 substrates, the thermal degradation is mainly due to the CrO x microflake formation from SS 310 substrates rather than the coatings, which can be suppressed by preoxidizing the surface of SS 310. With lower emittance matrix material and further optimization of pigment NP stoichiometry, concentration, and coating thickness, it is promising to achieve an optimized thermal efficiency ≥92.5% with long-term thermal stability at 750 °C for Generation 3 CSP systems.
Impacts of hybridization and forecast errors on the probabilistic capacity credit of batteries
Battery storage is increasingly identified as being among the least-cost mix of technologies in the evolving U.S. electricity mix. This study explores the marginal capacity credit of batteries using a probabilistic, reliability-based, effective firm capacity method, which we apply for multiple battery power ratings, durations, coupling types, deployment locations, and dispatch profiles within a test system that is based on the Texas Interconnection in the year 2024. We find that the capacity credits for all battery durations depend on their ability to predict the timing of reliability events. Even 1-2 h forecast errors - resulting in early or delayed battery discharging relative to the onset of a reliability event - lead pronounced capacity credit reductions, especially for 4-h duration batteries. Coupling batteries with solar mitigates the uncertainty associated with a shorter-duration battery's availability during reliability events, primarily due to the relatively high solar capacity credit in our test system. Coupled (or hybrid) system designs with oversized solar arrays, the ability to charge the coupled battery with grid energy, and larger batteries lead to the greatest capacity credit benefits of hybridization. We do not see evidence that the hybrid capacity credit exceeds the sum of the separate battery and solar capacity credits.
Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach
The escalating energy demand and the adverse environmental impacts of fossil-fuel use necessitate a shift towards cleaner and renewable alternatives. Concentrated Solar Power (CSP) technology emerges as a promising solution, offering a carbon-free alternative for power generation. The efficiency and profitability of CSP depend on the Direct Normal Irradiance (DNI) component of solar radiation; hence, accurate DNI forecasting can help optimize CSP plants’ operations and performance. The unpredictable nature of weather phenomena, particularly cloud cover, introduces uncertainty into DNI projections. Existing DNI forecasting models use meteorological factors, which are both challenging to estimate numerically over short prediction windows and expensive to model through data at a sufficiently high spatial and temporal resolution. This research addresses the challenge by presenting a novel approach that formulates DNI prediction as a multi-class classification problem, departing from conventional regression-based methods. The primary objective of this classification framework is to identify optimal periods aligning with specific operational thresholds for CSP plants, contributing to enhanced dispatch optimization strategies. We model the DNI classification problem using four advanced deep neural networks – rectified linear unit (ReLU) networks, 1D residual networks (ResNets), bidirectional long short-term memory (BiLSTM) networks, and transformers – achieving accuracies up to 93.5% without requiring meteorological parameters.