Path to zero carbon emissions: Models show that to avert dangerous levels of climate change, global carbon dioxide emissions must fall to zero later this century. Most of these emissions arise from energy use. Daviset al.review what it would take to achieve decarbonization of the energy system. Some parts of the energy system are particularly difficult to decarbonize, including aviation, long-distance transport, steel and cement production, and provision of a reliable electricity supply. Current technologies and pathways show promise, but integration of now-discrete energy sectors and industrial processes is vital to achieve minimal emissions.Science, this issue p.eaas9793

Energy storage at all timescales, including the seasonal scale, plays a pivotal role in enabling increased penetration levels of wind and solar photovoltaic energy sources in power systems. Grid-integrated seasonal energy storage can reshape seasonal fluctuations of variable and uncertain power generation by reducing energy curtailment, replacing peak generation capacity, and providing transmission benefits. Most current literature focuses on technology cost assessments and does not characterize the potential grid benefits of seasonal storage to capture the most cost-effective solutions. We propose a model-based approach for comprehensive techno-economic assessments of grid-integrated seasonal storage. The approach has two major advantages compared to those presented in the literature. First, we do not make assumptions about the operation of the storage device, including annual cycles, asset utilization or depth of discharge. Rather, a model is used to calculate optimal storage operation profiles. Second, the model-based approach accounts for avoided power system costs, which allows us to estimate the cost effectiveness of different types of storage devices. We assess the cost competitiveness of three specific storage technologies including pumped hydro, compressed air, and hydrogen seasonal storage and explore the conditions (cost, storage duration, and efficiency) that encourage cost competitiveness for seasonal storage technologies. This study considers the Western U.S. power system with 24% to 61% of variable renewable power sources on an annual energy basis (up to 83.5% of renewable energy including hydro, geothermal, and biomass power sources). Our results indicate that for the Western U.S. power system, pumped hydro and compressed air energy storage with 1 day of discharge duration are expected to be cost-competitive in the near future. In contrast, hydrogen storage with up to 1 week of discharge duration could be cost-effective in the near future if power and energy capacity capital costs are equal to or less than ∼US$1507 kW⁻¹ and ∼US$1.8 kWh⁻¹ by 2025, respectively. However, based on projected power and energy capacity capital costs for 2050, hydrogen storage with up to 2 weeks of discharge duration is expected to be cost-effective in future power systems. Moreover, storage systems with greater discharge duration could be cost-competitive in the near future if greater renewable penetration levels increase arbitrage or capacity value, significant energy capital cost reductions are achieved, or revenues from additional services and new markets—e.g., reliability and resiliency—are monetized.

Achieving high renewable energy penetrated power systems requires considerable operational flexibility to hedge the variability and uncertainty of variable renewable energy (VRE) generation. Compared with VRE sources, concentrating solar power (CSP) is an emerging controllable renewable generation technique that utilizes solar thermal power to generate electricity. The operational dispatchability of CSP would contribute to the power system transition toward high renewable penetration. In this paper, we explore how the generation portfolio will change toward high renewable energy penetrations, how much cost is involved, and what role CSP will play in realizing a high renewable energy penetrated power system. This study relies on a stochastic two-stage generation and transmission expansion planning model with CSP plants. The model captures the uncertainty and variability of renewable generation and the flexibility limits of thermal plants. With the target of achieving a renewable-dominated minimum-cost system with an expected renewable energy penetration level, the investments of both generation and transmission facilities are optimized. A case study on IEEE test systems with renewable technology cost data in 2050 is performed to analyze the value of CSP toward high renewable energy penetrated power systems.

This paper presents a new analog ensemble method for day-ahead regional photovoltaic (PV) power forecasting with hourly resolution. By utilizing open weather forecast and power measurement data, this prediction method is processed within a set of historical data with similar meteorological data (temperature and irradiance), and astronomical date (solar time and earth declination angle). Further, clustering and blending strategies are applied to improve its accuracy in regional PV forecasting. The robustness of the proposed method is demonstrated with three different numerical weather prediction models, the North American Mesoscale Forecast System, the Global Forecast System, and the Short-Range Ensemble Forecast, for both region level and single site level PV forecasts. Using real measured data, the new forecasting approach is applied to the load zone in Southeastern Massachusetts as a case study. The normalized root mean square error (NRMSE) has been reduced by 13.80%-61.21% when compared with three tested baselines.

The integration of variable renewable energy (VRE) generation, i.e. wind power and solar photovoltaic, brings significant uncertainty for the power system operation. Different with VRE techniques, concentrating solar power (CSP) is an appealing renewable generation technology due to its dispatch-ability through the use of thermal energy storage (TES) and is thus expected to play a significant role in high renewable energy penetrated power systems. In this paper, we propose a look-ahead stochastic unit commitment model to operate power systems with CSP under high renewable energy penetration. It has a three-stage structure. The first stage optimizes the operational decisions in a day-ahead framework based on forecasts; the second stage minimizes the expected generation cost for possible realizations in the real-time; and the third stage accounts for look-ahead operation in future operating days. This paper has a dual purpose: 1) exploring how CSP plants operate in high renewable penetrated power systems and 2) analyzing the benefits of CSP in accommodating VRE generation. A case study on a modified IEEE RTS-79 system with actual solar and wind power data is provided to validate the proposed method.