Near-optimal energy planning strategies with modeling to generate alternatives to flexibly explore practically desirable options

The dawn of urban energy planning – Synergies between energy and urban planning for São Paulo (Brazil) megacity

Strategic municipal energy planning in Sweden – Examining current energy planning practice and its influence on comprehensive planning

Multi-stage stochastic planning of regional integrated energy system based on scenario tree path optimization under long-term multiple uncertainties

It is significant to adopt renewable energy systems in building industry as one of the most important sectors in energy consumption. Level of energy consumption in building industry in contemporary architecture of developing countries such as Iran enjoys much more interest than other parts especially developed countries. Literature review of the research show that adoption of renewable energy systems has a lot to do with politics, policies, law, finances and market. While renewable energy finance and energy economics is in need of building sector to focus on adoption of renewable energy systems more than other sectors. It is very important to explain that high-performance architecture and planning requires some kinds of combined strategies to enhance the level of energy efficiency. The main scope of the research is to show concept of smart hybrid strategies for high-performance architecture and planning in case of Tehran, as one of the most important and efficient factors toward efficient adoption of renewable energy systems. The most important questions of the research are: 1- How socio-cultural and economic aspects of developing countries affect the idea of energy efficient architecture and planning? 2- What are the most important characteristics of high-performance architecture and planning for developing countries? Regarding to the hypothesis of the paper: 1- The idea of energy efficient architecture and planning in developing countries requires combined and smart hybrid strategies to meet energy efficient architecture and planning. 2- Smart hybrid strategies for high-performance architecture and planning consists of smart ventilation, photovoltaic systems, heat pumps, thermal storage, isolated building systems, low-e windows, efficient envelope and hyper - controlled openings.

This study addresses the pressing need for sustainable energy infrastructure in the Eastern Nile Basin region, focusing on the integration of Floating Solar Photovoltaics (FPVs) in long-term energy planning. FPVs offer advantages over land-based photovoltaics, such as reducing capital costs by utilizing existing infrastructure at hydropower dams and reducing evaporation. Given the region's growing population and high competition for water, our research introduces a novel framework that explores the dual benefits of water conservation and reduced land use, alongside policy targets for lowering carbon emissions through increased integration of renewables in the power mix.The study advances existing models by incorporating FPV technology into the OSeMOSYS tool, an open-source model for optimizing national energy generation mixes. Our research presents a spatially explicit framework for long-term  energy system planning that integrates land use and water conservation metrics at reservoirs within the energy planning process. The role of FPVs in the region’s energy pathways is evaluated by endogenizing the costs of CO2 emissions and land use, while considering water savings. Our analysis develops and implements a new methodology for land-use accounting and pricing, and assesses the potential of FPVs to reduce evaporation across a network of hydropower reservoirs. This expanded modeling framework is then utilized to analyze various scenarios, including different hydrological regimes under CMIP climate change projections and policy measures such as the introduction of taxes on carbon emissions and land-use, and regional electricity trade links.Results indicate that FPVs can cost-effectively provide up to 3% of the region's electricity generation by 2065, saving up to 376 million cubic meters of water annually. Scenarios introducing carbon and land-use taxes increase FPV's share in the power generation mix to 4.5% and enable earlier FPV deployment. While climate impacts minimally affect FPV's role, the technology slightly reduces CO2 emissions (0.4%) and land use (1.6%) in the baseline scenario without taxes. A carbon tax alone reduces emissions by 11-23% but raises land use by up to 8% due to increased wind, hydro, and solar deployment. Land tax alone reduces land use by 5-8% with minimal impact on emissions. However, combining land and carbon taxes reduces emissions (by 12% to 22%) and land use (a decrease of 1.6% or an increase of 1.2%). The optimal locations for FPV deployment are identified as Lake Nasser (2.1 GW), Renaissance Dam (6.4 GW), and Merowe Dam (1.2 GW), leveraging existing hydropower infrastructure. These findings demonstrate that FPVs represent a promising adaptation strategy for energy planning offering multiple co-benefits including reduced water evaporation, efficient land use, and emissions mitigation, particularly when supported by appropriate environmental pricing policies.

Low emissions analysis platform model for renewable energy: Community-scale case studies in Nigeria

Wildlife planning for renewable energy must cope with the uncertainties of potential wildlife impacts. Unfortunately, the environmental policies which instigate renewable energy and those which protect wildlife are not coherently aligned-creating a green versus green dilemma. Thus, climate mitigation efforts trigger renewable energy development, but then face substantial barriers from biodiversity protection instruments and practices. This article briefly reviews wind energy and wildlife interactions, highlighting the lively debated effects on bats. Today, planning and siting of renewable energy are guided by the precautionary principle in an attempt to carefully address wildlife challenges. However, this planning attitude creates limitations as it struggles to negotiate the aforementioned green versus green dilemma. More adaptive planning and management strategies and practices hold the potential to reconcile these discrepancies to some degree. This adaptive approach is discussed using facets of case studies from policy, planning, siting, and operational stages of wind energy in Germany and the United States, with one case showing adaptive planning in action for solar energy as well. This article attempts to highlight the benefits of more adaptive approaches as well as the possible shortcomings, such as reduced planning security for renewable energy developers. In conclusion, these studies show that adaptive planning and operation strategies can be designed to supplement and enhance the precautionary principle in wildlife planning for green energy.

Electrical energy planning of modern electric power system requires great attention for the air pollution problem. This is in accordance to the increasing tendency to optimise the production processes in the respect of their sustainability. Moreover, the advent of the deregulation in the electric energy market has involved substantial modifications of philosophy in planning and managing the electrical power system. These aspects together offer new challenge in defining proper system study methodologies and suitable planning strategies. One of the most complex problems in the planning strategies is the data management uncertainty related to load forecasting and environmental modelling. In this paper a robust optimal planning procedure is formalized, taking adequately into account the problem of stochastic variables presence, allowing to satisfy the air quality and load demand forecast constraints in spite of uncertainties. The proposed approach addresses generation planning, where the various aspects have been integrated to solve the problem in a feasible and realistic way. A numerical application to an electrical power system with respect to a regional area is reported in the final part of the paper, showing the applicability of the proposed methodology.

The first feasible step towards clean heating transition in urban agglomeration: A case study of Beijing-Tianjin-Hebei region

Stakeholders' awareness of urban form effects on rooftop solar photovoltaic in Ghana: Implications for integrated solar energy and urban planning

To address the challenges in new power systems, such as wind and photovoltaic curtailment and insufficient energy storage incentives, caused by imbalances in the regulation of power supply and demand, the academic community has proposed the substitute power product (SPP) market, which is based on the trading of prescribed generation shapes over defined periods. However, there is currently no literature addressing the optimization strategies for market participants within the SPP market trading model. To fill this gap, this study introduces, for the first time, an energy storage planning and optimization operation strategy for wind and photovoltaic energy stations within this market framework. First, the basic trading process and related concepts of the SPP market are introduced. Then, based on the operational logic of the SPP market, a multi-stage energy storage planning and operation strategy is proposed for wind and photovoltaic stations. This strategy integrates a two-level model with a multi-scenario stochastic planning model to optimize the storage capacity and power allocation of renewable energy stations under uncertainty. Subsequently, relevant metrics for the SPP market are introduced, and these metrics are used to quantitatively analyze the feasibility of cooperation among different renewable energy stakeholders in the market. Finally, the impact of regulation power prices on the decision-making of renewable energy stations participating in the SPP market is explored. The findings of this study provide new energy producers with a preliminary optimization solution for energy storage configuration and operation under the new trading model, promoting their participation in the SPP market and contributing to the acceleration of the low-carbon transformation of power systems.

In order to actively respond to the “14th Five-Year Plan,” the PGA algorithm is used to develop a new energy planning strategy in this paper. The project can make full use of my country's abundant renewable energy resources, encourage energy conservation and reduction of emissions, improve the energy structure's low-carbon level, support the development of smart green energy, and achieve ecological civilization construction. This solution can show users how much greenhouse gas emissions can be reduced through some environmental changes, as well as the basic issues of meeting the future energy needs. It can display the benefits, costs, and emissions data under different scenarios in the future and use the scenario demonstration method to show energy planning to make energy data more vivid. It allows people, technicians, and decision makers to understand what will happen to China's carbon emissions over time in the next 15 years. This paper innovatively combines a particle swarm optimization algorithm with a genetic algorithm and designs a PGA algorithm for path optimization. In terms of carbon emission reduction, comparative trials demonstrate that the PGA algorithm's path optimization is 58.06 percent greater than the genetic algorithm; In terms of cost, the PGA algorithm's path optimization is 15.72% less expensive than the genetic algorithm's. This article provides a reference path for selecting the best results for future energy planning schemes and provides a new strategy for the “14th Five-Year” energy plan.

Design and application of energy planning scheme based on genetic algorithm

Hybrid stochastic robust optimization and robust optimization for energy planning – A social impact-constrained case study

The stability and efficiency of the energy system’s use have been greatly impacted by the large percentage of new energy access. Therefore, an integrated energy collaborative planning technique based on the geographical correlation of solar and wind is presented to ensure the stability of the energy system and enhance energy consumption. This method first uses an improved particle swarm optimization k-means algorithm to cluster the spatial correlations of wind and solar scenarios to obtain typical scenarios as inputs for constructing an energy planning model. Based on this, a comprehensive energy system planning model is constructed. Finally, by using the target cascade analysis method to solve the model, a comprehensive collaborative energy planning scheme can be obtained. The experimental results indicated that the improved particle swarm-K-means algorithm was able to generate 50 typical scenarios of wind power and photovoltaic. Scenarios 1–48 were the clustering centers, while scenarios 49 and 50 were the extreme scenarios of wind power output. Compared to the energy planning models based on cloud model-fuzzy c-means clustering and fast search and clustering of density peak values, the cost of the proposed integrated energy planning model was reduced by 2.58% and 14.55% respectively. Meanwhile, in the energy planning method proposed by the study, the exchanged power of transmission and distribution grids was up to 8.1 MW. The exchanged power of distribution and gas distribution grids was −0.8 MW, and the energy utilization efficiency was high. The aforementioned findings demonstrate that the study’s integrated energy planning model can successfully lower energy planning expenses while guaranteeing the integrated energy system’s overall benefit.