Peak load shaving is a practical alternative to over-designing the power system to meet maximum demand. In this context, grid-connected photovoltaic system (GCPVS) is an effective solution across regional and national scales. The tilt (β) and azimuth (ψ) angles of fixed-structure GCPVS are conventionally optimized to ensure maximum annual yield or minimum electricity costs. This highlights a gap that no existing study has optimized the orientation of PV modules from a peak load shaving perspective. To address this gap, for the first time, this paper proposes a multi-scale, search-based optimization methodology to determine the tilt and azimuth angles for maximizing peak load shaving. The proposed approach is applied to a 10 kW GCPVS at two commercial buildings in Delft, Netherlands, and Mashhad, Iran. The method finds β = 24° and ψ = 45° as an optimum solution in Delft with a heating-dominated load during cold afternoons. For Mashhad, the GCPVS shaves summer noon air conditioning-based peak load with β = 12° and ψ = −10°. The results highlight that the proposed method ensures maximum peak load shaving of the GCPVS, even with a non-optimized annual energy yield. Also, the substantial dependency of the optimal angles on the local load profile, GCPVS characteristics, and the site's solar potential is demonstrated. Although the effectiveness of this method is shown on two commercial buildings, it can be applied to any geographical scope from regional to national scales, making it a multi-scale model. The proposed model is markedly practical to the policymakers, who can design policies to incentivize GCPVS owners to operate their system for maximum peak load shaving, thereby increasing the overall economic efficiency of the power system.

Carbon capture and storage (CCS) has substantial potential for deep decarbonization of the steel sector. However, long-term transformations within this sector lead to significant changes in steel units, posing challenges for CCS deployment. Here, we integrate sector-level transformation pathways by 2060 to simulate the distribution of China’s steel units and generate optimal CCS deployment schemes using a source-sink matching model. Results indicate that CCS accounts for 31.4-40.7% of carbon mitigation effects in China’s steel sector by 2060. Following the sector-level pathways, over 650 steel units will either be eliminated or retrofitted. The optimal CCS deployment schemes can achieve carbon mitigation effects of 472.4-609.6 Mt at levelized costs of 187.4-193.5 Chinese Yuan t⁻¹ CO₂, demonstrating cost-effectiveness under future carbon price levels. Nevertheless, the proposed schemes will lead to energy and water consumption of 951.0-1427.3 PJ and 1.60-1.69 million m³, respectively, posing a risk of resource scarcity. These insights inform the development of CCS implementation strategies in China’s steel sector and beyond, promoting deep decarbonization throughout society.

Coordinating the interactions between increasingly interconnected energy sectors and carriers can lead to an efficient integration of variable renewable energy (VRE) resources, and a more cost-efficient energy transition. This paper proposes a model coupling approach that uses a market-based mechanism to efficiently coordinate the interactions among electricity, heat, and (hydrogen) gas systems, and (near) optimally schedule flexibility to maximize social welfare. The proposed approach is benchmarked against traditional co-optimization, and is shown to achieve comparable results with a moderate "optimality gap"in terms of reduction in system costs, peak load, and VRE curtailment. Its added value is the ability to enable each system to interact in an integrated energy system and locally optimize their decisions without sharing confidential information. The practical implication of this new approach is to provide a modeling environment where system operators and flexibility aggregators can obtain insights into the impacts of decarbonization of other parties on their systems - thereby avoiding myopic operational or investment decisions.