An investigation on the feasibility of Positive Energy Districts in hot and humid climate: a case study in Lavan Island

The transition toward a low-carbon built environment increasingly relies on the development of Positive Energy Districts (PEDs), which integrate renewable energy solutions at the urban scale. Given their inherent complexity and multidisciplinary nature, PEDs require a holistic and integrated approach to infrastructure planning and development. This study develops and applies a Multi-Attribute Decision-Making (MADM) model to identify the most suitable renovation strategies for transforming existing urban areas into PEDs. Based on Multi-Attribute Value Theory, the model implements the Analytical Hierarchy Process (AHP) to assign weights to various evaluation criteria and engages a panel of thirteen PED experts through a structured questionnaire. The methodology was applied to the University of Palermo campus, enabling group decision-making while minimizing subjectivity. The campus’s current energy performance was simulated using the EnergyPlus dynamic building energy simulation tool and multiple renovation scenarios were evaluated and compared. Each scenario was assessed using a set of Key Performance Indicators (KPIs), grouped into two main domains: (i) Energy and Environmental and (ii) Socio-Economic and Urban Planning. A composite Positive Energy District Sustainability Index was developed to facilitate scenarios’ comparison and ranking. The model demonstrated its potential in supporting decision-making processes for PED planning by integrating experts’ knowledge, performance simulation and multi-criteria evaluation. The case study confirmed its capability to guide renovation strategies toward sustainable and replicable PED configurations.

As cities grow rapidly and energy needs increase, shaping an effective energy transition is a top priority towards urban sustainability and smart development. This study attempts to answer three key research questions that can help city authorities, planners and interested agents simplify and increase the transparency of Key Performance Indicators (KPIs) selection for smart city and communities (SCC) projects focusing on energy transition and creation of Positive Energy Districts (PEDs): Question 1: “What resources are available for extracting such KPIs?”; Question 2: “Which of those KPIs are the most suitable for assessing the energy transition of smart city projects and PED-related developments?” and Question 3: “How can a project-specific shortlist of KPIs be developed?”. Answering these questions can also serve as a major first step towards a “universal” KPI selection procedure. In line with this purpose, an experiential approach is presented, capitalizing on knowledge and lessons learned from an ongoing smart city project in Europe (POCITYF) that focuses on PED deployment. Under this framework, a) a review of smart city KPI frameworks has been conducted, resulting in a pool of 258 indicators that can potentially be adopted by smart city projects; b) eight key dimensions of evaluations were extracted, setting a holistic performance framework relevant to SCCs; c) a detailed evaluation process including pre-determined criteria and city-needs feedback was applied to shortlist the KPI pool, leading to a ready-to-be-used, project-specific list of 63 KPIs and d) KPIs were sorted and analyzed in different granularity levels to further facilitate the monitoring procedure. The experiential procedure presented in this study can be easily adapted to the needs of every smart city project, serving as a recommendation guide.

During the last decade, increasing attention has been paid to the emerging concept of Positive Energy Districts (PED) with the aim of pushing the transition to clean energy, but further research efforts are needed to identify design approaches optimized from the point of view of sustainable development. In this context, this literature review is placed, with a specific focus on environmental sustainability within innovative and eco-sustainable districts. The findings show that some sustainability aspects such as sustainable food, urban heat islands mitigation and co-impacts, e.g., green gentrification, are not adequately assessed, while fragmented thinking limits the potential of circularity. In this regard, targeted strategies should be developed. On the other hand, the Key Performance Indicators framework needs some integrations. In this direction, indicators were suggested, among those defined in the Sustainable Development Agenda, the main European standards and initiatives and the relevant literature experiences. Future outlooks should be directed towards: the harmonization of the Life Cycle Assessment in PEDs with reference to modeling assumptions and analysis of multiple impacts; the development of dynamic environmental analyses taking into account the long-term uncertainty due to climate change, data availability and energy decarbonization; the combination of Life Cycle Assessment and Key Performance Indicators based techniques, from a holistic thinking perspective, for a comprehensive design environment and the analysis of the contribution of energy flexibility approaches on the environmental impact of a project.

Cities consume two-thirds of the energy supply, and 70% of carbon dioxide equivalent emissions come from urban environments. Positive Energy Districts are innovative tools to achieve energy and climate neutrality in cities. Positive Energy Districts are regions or neighbourhoods with a positive annual energy balance, obtained mainly through energy efficiency and energy generation from renewables. Urban Waterfronts are extended areas close to the sea, which makes them suitable for several types of production with renewables, therefore seeming to be a suitable location to develop Positive Energy Districts. This paper proposes a method that combines strategic planning for project management and the procedure for energy audits to design the optimal district configuration. The study presents and analyses the case of La Marina de València, a district in a Mediterranean city. Three strategic scenarios, both technically feasible and with a positive energy balance, are presented. All the alternatives include PV and switching to light-emitting diode in lighting. The different strategies presented together with a sensitivity analysis facilitate the decision-making process in energy planning and establish a common pathway to achieve Positive Energy Districts in Urban Water Fronts. The results suggest that urban waterfronts are uniquely suited to achieve a positive annual energy balance, thus emerging as a crucial springboard to provide traction to the positive energy districts policy agenda.

Ten questions on tools and methods for positive energy districts

Positive Energy Districts (PEDs) are integral to achieving sustainable urban development by enhancing energy self-sufficiency and reducing carbon emissions. This paper explores energy balance calculations in four diverse case study districts within different climatic conditions—Fiat Village in Settimo Torinese (Italy), Großschönau (Austria), Beursplain in Amsterdam (Netherlands), and Lunca Pomostului in Reşiţa (Romania)—as part of the SIMPLY Positive project. Each district faces unique challenges, such as outdated infrastructure or heritage protection, which we address through tailored strategies including building renovations and the integration of renewable energy systems. Additionally, we employ advanced simulation methodologies to assess energy performance. Simulation results highlight the significance of innovative technologies like photovoltaic-thermal (PVT) systems, application of demand-side actions, and flexible grid usage. Furthermore, mobility assessments and resident-driven initiatives demonstrate the critical role of community engagement in reducing carbon footprints. This study underscores the adaptability of PED frameworks across varied urban contexts and provides actionable insights for scaling similar strategies globally, supporting net-zero energy targets.

Dynamic models of higher education Key Performance Indicators (KPI) help in understanding how internal and external factors affect future KPI achievement. This study aims to construct a dynamic model of university KPI variables and estimate model parameters value. Several used steps to achieve goals are problem definition, variables, model formulation, prerequisite estimation, and conformity analysis. This model involves eight KPIs and three types of funding. Three optimization methods used are Type I constrained optimization, Type II constrained optimization, and unconstrained optimization. The results showed that the percentage of graduates getting decent jobs (KPI 1) in the future year is strongly influenced by two KPIs, namely the work of lecturers (KPI 5) and study programs with international accreditation (KPI 8). The existence of active practitioners (KPI 4) opens opportunities for international cooperation (KPI 6) and collaborative learning (KPI 7). Significant investment in improving the quality of lecturers and the right allocation of funds has proven to impact achieving the goals of higher education positively.

This paper presents Microgrid (MG) optimization using Genetic Algorithm. The MG model is based on renewable energy sources (wind turbine) and gas generator. The algorithm objective is determine the optimal size of combined wind and gas generator to satisfy a given Key Performance Indices (KPIs). The selected KPIs describe both dynamic and static performance of MG. The KPIs describing the dynamic performance includes Total Harmonic Distortion (THD) and power factor (PF) in presence of disturbance and load variation. The static KPIs includes power shortage, initial cost, running cost and CO 2 emission. The two KPIs groups (dynamic and static) have different time frame. The dynamic KPIs are examined by applying load disturbance to MG and observe its effect over few seconds (according to MG average time constant). The static KPIs are examined by applying load and power generation profiles during one full year period. Hence, it is not feasible to combine both static and dynamic simulation using one model. Accordingly, to allow one optimization process based on static and dynamic KPIs, two simulation models have been created with two separate simulation environments. The static simulation uses simplified efficiency model of the power components presented in MG and the system is subjected to load and wind profiles to evaluate the static KPIs. The dynamic simulation uses detailed dynamic model with load disturbance. The optimization process utilizes a single fitness function which combines the dynamic and static PKIs with weighting factors. Results of optimization are presented and the KPIs of the optimized MG is provided.

IntroductionAmid the rising interest in sustainable urban development, Positive Energy Districts (PEDs) have become a focus of research. This study examines the dynamic processes that influence the development and scalability of PEDs from a co-evolutionary business ecosystem perspective.MethodsTo delve into the dynamics of Positive Energy Districts, we applied the business ecosystem framework to a real-world case study, namely the Hunziker Areal. Our research methodology involved the development and validation of a high-level conceptual model. This was achieved through workshops and guided interviews with experts engaged in pilot and research projects related to PEDs.ResultsThe study highlights the significance of employing a systemic approach to evaluate the potential of PEDs in enhancing housing sustainability while creating value for diverse stakeholders. Through the utilization of causal loop diagrams, key feedback loops explaining the diffusion of PEDs are identified. Moreover, the study reveals varying perceptions of PED utility among stakeholders, who assess the impact using different Key Performance Indicators (KPIs) such as CO2 target achievement and well-being. Key factors influencing technology adoption, such as low prosumer electricity unit cost, are also identified.DiscussionOur findings further shed light on crucial aspects affecting value capture and the attractiveness of the ecosystem to investors. Additionally, the study underscores the critical role of supportive policies and regulations in facilitating the diffusion and scalability of Positive Energy Districts.

In this paper, we provide a view of the ongoing PEDRERA project, whose main scope is to design a district simulation model able to set and analyze a reliable prediction of potential business scenarios on large scale retrofitting actions, and to evaluate the overall co-benefits resulting from the renovation process of a cluster of buildings. According to this purpose and to a Positive Energy Districts (PEDs) approach, the model combines systemized data—at both building and district scale—from multiple sources and domains. A sensitive analysis of 200 scenarios provided a quick perception on how results will change once inputs are defined, and how attended results will answer to stakeholders’ requirements. In order to enable a clever input analysis and to appraise wide-ranging ranks of Key Performance Indicators (KPIs) suited to each stakeholder and design phase targets, the model is currently under the implementation in the urbanZEB tool’s web platform.

The economic operation of hydrothermal systems is one of the most challenging problems in the area of power systems because it is a non-linear and non-convex problem. The use of heuristic optimization methods are an attractive alternative to traditional deterministic methods by offering excellent quality in their results with an implementation that is characterized by having a simple concept. This document solves the hydrothermal coordination problem considering the hydraulic coupling of hydroelectric power plants and the valve point effect in thermal power plants using genetic algorithms (GA), the novel grey wolf algorithm (GWO) and particle swarm optimization method (PSO). In addition, different Key Performance Indicators are proposed to validate the performance achieved by each heuristic technique applied to the planning of the economic dispatch of a multiple set of thermal and hydroelectric power plants for a daily scheduling horizon of 24-hour.

• Condensation risk limits radiant cooling in a hot and humid climate. • Infrared-transparent membranes prevent condensation on radiant cooling surfaces. • Radiant surface at 4 °C below dew point reduced discomfort hours by 3–6%. • Experiments-modeling should target lower radiant surface temperatures. • Solar gains significantly affected radiant cooling effectiveness. Condensation risk has limited the deployment of radiant cooling systems for improving thermal comfort in hot and humid climates. Infrared-transparent membrane films emerged as a viable alternative to improve hybrid radiant cooling by preventing moisture condensation. This case study assesses the potential of membrane-assisted radiant cooling in a rural school building in Santa Lucia, Atlántico (Colombia), a location with a hot and humid tropical climate throughout the year. The study evaluates different cooling strategies, including natural ventilation, mechanical ventilation, hybrid radiant cooling, and membrane-assisted radiant cooling using EnergyPlus® software. Results for a radiant surface temperature of 4 °C below the dew point show that membrane-assisted radiant cooling could reduce annual discomfort hours by 3–6% compared to conventional radiant cooling. Further experimental and modeling studies should focus on the lowest achievable temperature below the dew point without condensation. Our results suggest that radiant cooling with infrared-transparent membranes is a potentially cost-effective alternative for improving thermal comfort in rural school buildings in hot and humid climates.

In this article, a novel approach for assessment and ranking of maintenance process indicators as well as maintenance cost indicators and maintenance equipment indicators using the fuzzy sets approach and genetic algorithms is presented. Weight values of these indicators are defined using the experience of decision makers from analyzed small and medium enterprises (total number of 197 persons) and calculated using the fuzzy sets approach. In the second step, a model for ranking and optimization of maintenance performance indicators and small and medium enterprises by using genetic algorithm is presented. The presented approach enables multi-objective optimization of selected key performance indicators in the scope of optimization of maintenance performances. The value of optimization was tested on a group of small and medium enterprises which proved that improvement of maintenance performance could be more significant (or performed at the shorter period of time) if the specific key performance indicators were targeted for improvement. The presented solution could provide identification of strengths and weaknesses (comparing key performance indicators), learning from a leading organization (in prioritization of key performance indicator improvement) and improvement of maintenance performance.

The renewable energy sources (RES) are globally recognized as a suitable option for sustainable development in many off-grid applications. Recently, the integrated systems with two or more RES are being paid great attention for electrification of isolated areas and found to be an acceptable solution rather than uneconomical grid extension. In the present study, the integrated renewable energy system (IRES) model is developed using solar, wind, biomass and biogas energy sources to meet the electricity demand of the isolated rural community of Khatisitara village of Gujarat state in India. The operational strategy of IRES model is developed considering the distribution network losses as a system design parameter. The developed IRES model is optimized for minimum net present cost of the system using particle swarm optimization (PSO) algorithm in MATLAB environment. The well-established genetic algorithm (GA) was used to validate the optimization results obtained from PSO. Further, the effects of distribution losses (DL) on the system sizing, reliability and economy have been evaluated. The sensitivity analysis was performed to assess the effect of economically influencing parameters on the developed model. Finally, the break-even analysis was performed for the grid extension distance to examine the economic feasibility of IRES against grid extension. The simulation result shows that the impacts of DL on IRES reliability and economy are significant. Further analysis shows that the IRES using locally available renewable energy sources is a feasible option for rural electrification in considered study area rather than grid extension.

Monitoring is one of the key points in any retrofitting project because, thanks to its results, both the current and improved status can be analysed. Within this context, not only monitoring is important, but also consistency and coherency of data, as well as aggregated information is required. In that sense, surveillance methods and Key Performance Indicators (KPIs) play a pivotal role. Thus, the project iNSPiRe develops an automated monitoring system able to store daily information, as well as automatically calculate KPIs by means of database technologies and determines alarms at different levels. With the obtained results, iNSPiRE aims at conceiving, developing and demonstrating Systemic Renovation Packages, through the innovative integration of envelope technologies, energy generation (including Renewable energy systems (RES) integration), energy distribution, lighting and comfort management systems into deep energy renovation of buildings, both in the residential and tertiary sectors. Then, the combination of the automated monitoring framework and the renovation packages, the goal is to achieve energy consumption for the building lower than 50 kWh/m2/year.