Developing smart energy communities around fishery ports: toward zero-carbon fishery ports

Air quality and energy consumption are among the top ten environmental priorities in seaports as stated by the European Sea Ports Organization. Globally, it is estimated that 15% of energy consumption can be attributed to refrigeration and air conditioning systems in fishing activities. There is a real need to understand energy usage in fishery ports to help identify areas of improvements, with a view to optimize energy usage and minimize carbon emissions. In this study, we elaborate on ways in which a simulation capability can be developed at the community level with a fishery port, using a real-world case study seaport in Milford Heaven (Wales, UK). This simulation-based strategy is used to investigate the potential of renewable energy, including local solar farms, to meet the local power demand. This has informed the development of a simulation-based optimization strategy meant to explore how smart energy communities can be formed at the port level by integrating the smart grid with the local community energy storage. The main contribution of the paper involves a co-simulation environment that leverages calibrated energy simulation models to deliver an optimization capability that (a) manages electrical storage within a district an environment, and (b) promotes the formation of energy communities in a fishery port ecosystem. This is paving the way to policy implications, not only in terms of carbon and energy reduction, but also in the formation and sustained management of energy communities

SCADA Office Building Implementation in the Context of an Aggregator

This paper at first presents an aggregation model including optimization tools for optimal resource scheduling and aggregating, and then, it proposes a real implemented SCADA system in an office building for decision support techniques and participating in demand response events. The aggregator model controls and manages the consumption and generation of customers by establishing contract with them. The SCADA based office building presented in this paper is considered as a customer of proposed aggregation model. In the case study, a distribution network with 21 buses, including 20 consumers and 26 distributed generations, is proposed for the aggregator network, and optimal resource scheduling of aggregator, and performance of implemented SCADA system for the office building, will be surveyed. The scientific contribution of this paper is to address from an optimization-based aggregator model to a SCADA based customer.This work has received funding from the Projects: NetEffiCity (ANI|P2020 18015); FEDER Funds through COMPETE program; National Funds through FCT under project UID/EEA/00760/2013; H2020 DREAM-GO Project (Marie Sklodowska-Curie grant agreement No 641794).info:eu-repo/semantics/publishedVersio

Building Energy Modeling with OpenStudio : A Practical Guide for Students and Professionals

The energy, environmental, and societal challenges of the twenty-first century are here; they are crystal clear; and they are daunting. Our responses to those challenges are less clear, but one component at least is obvious—we need a better building stock, one that uses less energy, provides greater comfort and security, and houses and supports the economic activity of a rapidly growing and urbanizing population. One of the most powerful tools in our collective belts is building energy modeling (BEM), physics-based software simulation of building energy use given a description of the physical building, its use patterns, and prevailing weather conditions. BEM is a sine qua non tool for designing and operating buildings to the levels of energy efficiency that our future and present require. According to the AIA 2030 Commitment report, buildings designed using BEM use 20% less energy than those designed without it. BEM is also instrumental in developing and updating the codes, standards, certificates, and financial incentive infrastructure that supports energy efficiency in all building projects, including those that don’t directly use BEM. The OpenStudio project has been a driving force in the evolution of BTO’s BEM program. OpenStudio was BTO’s first truly open-source software project, a strategic direction that has influenced BTO’s entire BEM portfolio. Open-source is not an altruistic emergent enterprise. Successful open-source projects are funded, centrally managed, and resemble proprietary software projects in many structural and operational ways. Source control. Code reviews. Regression testing. Bug reporting and fixing. Pre-feature documentation. Post-feature documentation. The full Monty

Optimization-Based Home Energy Management System Under Different Electricity Pricing Schemes

This paper presents an optimization-based home energy management system, by taking advantages of renewable resources and energy storage system for optimally managing the energy consumption and generation of the house. The surplus of renewable generation will be stored in energy storage system or will be injected into the main grid. An optimization algorithm is developed for this system in order to minimize the electricity bill of the house considering electricity tariffs. Four home appliances are considered to be controlled by this system for reducing the consumption in critical periods. The outcomes of optimization problem are the optimal scheduling of the resources including renewable generation, energy storage system, consumption reduction, and power transactions with the grid. In the case study, the developed model will be employed in three different scenarios, which considers simple electricity prices and time-of- use tariffs in order to test and validate the performance of the developed model.The present work was done and funded in the scope of the following projects: H2020 DREAM-GO Project (Marie Sklodowska-Curie grant agreement No 641794); Project GREEDI (ANI|P2020 17822); and UID/EEA/00760/2013 funded by FEDER Funds through COMPETE program and by National Funds through FCT.info:eu-repo/semantics/publishedVersio

Energy engineering approach for rural areas cattle farmers in Bangladesh to reduce covid-19 impact on food safety

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This paper reports on the optimization of thin-film coating-assisted, self-sustainable, off-grid hybrid power generation systems for cattle farming in rural areas of Bangladesh. Bangladesh is a lower middle-income country with declining rates of poverty among its 160 million people due to persistent economic growth in conjunction with balanced agricultural improvements. Most of the rural households adopt a mixed farming system by cultivating crops and simultaneously rearing livestock. Among the animals raised, cattle are considered as the most valuable asset for the small-/medium-scale farmers in terms of their meat and milk production. Currently, along with the major health issue, the COVID-19 pandemic is hindering the world’s economic growth and has thrust millions into unemployment; Bangladesh is also in this loop. However, natural disasters such as COVID-19 pandemic and floods, largely constrain rural smallholder cattle farmers from climbing out of their poverty. In particular, small-and medium-scale cattle farmers face many issues that obstruct them from taking advantage of market opportunities and imposing a greater burden on their families and incomes. An appropriate measure can give a way to make those cattle farmers’ businesses both profitable and sustainable. Optimization of thin-film coating-assisted, self-sustainable, off-grid hybrid power generation system for cattle farming is a new and forward-looking approach for sustainable development of the livestock sector. In this study, we design and optimize a thin-film coating-assisted hybrid (photovoltaic battery generator) power system by using the Hybrid Optimization of Multiple Energy Resources (HOMER, Version 3.14.0) simulation tool. An analysis of the results has suggested that the off-grid hybrid system is more feasible for small-and medium-scale cattle farming systems with long-term sustainability to overcome the significant challenges faced by smallholder cattle farmers in Bangladesh

A METHODOLOGY FOR ENERGY OPTIMIZATION OF BUILDINGS CONSIDERING SIMULTANEOUSLY BUILDING ENVELOPE HVAC AND RENEWABLE SYSTEM PARAMETERS

Energy is the vital source of life and it plays a key role in development of human society. Any living creature relies on a source of energy to exist. Similarly, machines require power to operate. Starting with Industrial Revolution, the modern life clearly depends on energy. We need energy for almost everything we do in our daily life, including transportation, agriculture, telecommunication, powering industry, heating, cooling and lighting our buildings, powering electric equipment etc. Global energy requirement is set to increase due to many factors such as rapid industrialization, urbanization, population growth, and growing demand for higher living standards. There is a variety of energy resources available on our planet and non-renewable fossil fuels have been the main source of energy ever since the Industrial Revolution. Unfortunately, unsustainable consumption of energy resources and reliance on fossil fuels has led to severe problems such as energy resource scarcity, global climate change and environmental pollution. The building sector compromising homes, public buildings and businesses represent a major share of global energy and resource consumption. Therefore, while buildings provide numerous benefits to society, they also have major environmental impacts. To build and operate buildings, we consume about 40 % of global energy, 25 % of global water, and 40 % of other global resources. Moreover, buildings are involved in producing approximately one third of greenhouse gas emissions. Today, the stress put on the environment by building sector has reached dangerous levels therefore urgent measures are required to approach buildings and to minimize their negative impacts. We can design energy-efficient buildings only when we know where and why energy is needed and how it is used. Most of the energy consumed in buildings is used for heating, cooling, ventilating and lighting the indoor spaces, for sanitary water heating purposes and powering plug-in appliances required for daily life activities. Moreover, on-site renewable energy generation supports building energy efficiency by providing sustainable energy sources for the building energy needs. The production and consumption of energy carriers in buildings occur through the network of interconnected building sub-systems. A change in one energy process affects other energy processes. Thus, the overall building energy efficiency depends on the combined impact of the building with its systems interacting dynamically all among themselves, with building occupants and with outdoor conditions. Therefore, designing buildings for energy efficiency requires paying attention to complex interactions between the exterior environment and the internal conditions separated by building envelope complemented by building systems. In addition to building energy and CO2 emission performance, there are also other criteria for designers to consider for a comprehensive building design. For instance, building energy cost is one of the major cost types during building life span. Therefore, improving building efficiency not only addresses the challenges of global climate change but also high operational costs and consequent economic resource dependency. However, investments in energy efficiency measures can be costly, too. As a result, the economic viability of design options should be analysed carefully during decision-making process and cost-effective design choices needs to be identified. Furthermore, while applying measures to improve building performance, comfort conditions of occupants should not be neglected, as well. Advances in science and technologies introduced many approaches and technological products that can be benefitted in building design. However, it could be rather difficult to select what design strategies to follow and which technologies to implement among many for cost-effective energy efficiency while satisfying equally valued and beneficial objectives including comfort and environmental issues. Even using the state-of-the-art energy technologies can only have limited impact on the overall building performance if the building and system integration is not well explored. Conventional design methods, which are linear and sequential, are inadequate to address the inter-depended nature of buildings. There is a strong need today for new methods that can evaluate the overall building performance from different aspects while treating the building, its systems and surrounding as a whole and provide quantitative insight information for the designers. Therefore, in the current study, we purpose a simulation-based optimization methodology where improving building performance is taken integrally as one-problem and the interactions between building structure, HVAC equipment and building-integrated renewable energy production are simultaneously and dynamically solved through mathematical optimization techniques while looking for a balanced combination of several design options and design objectives for real-life design challenges. The objective of the methodology is to explore cost-effective energy saving options among a considered list of energy efficiency measures, which can provide comfort while limiting harmful environmental impacts in the long term therefore financial, environmental and comfort benefits are considered and assessed together. During the optimization-based search, building architectural features, building envelope features, size and type of HVAC equipment that belong to a pre-designed HVAC system and size and type of considered renewable system alternatives are explored simultaneously together for an optimal combination under given constraints. The developed optimization framework consists of three main modules: the optimizer, the simulator, and a user-created energy efficiency measures database. The responsibility of the optimizer is to control the entire process by implementing the optimization algorithm, to trigger simulation for performance calculation, to assign new values to variables, to calculate objective function, to impose constraints, and to check stopping criteria. The optimizer module is based on GenOpt optimization environment. However, a sub-module was designed, developed and added to optimization structure to enable Genopt to communicate with the user-created database module. Therefore, every time the value of a variable is updated, the technical and financial information of a matching product or system equipment is read from the database, written into simulation model, and fed to the objective formula. The simulator evaluates energy-related performance metrics and functional constraints through dynamic simulation techniques provided by EnergyPlus simulation tool. The database defines and organizes design variables and stores user-collected cost related, technical and non-technical data about the building energy efficiency measures to be tested during the optimization. An updated version of Particle Swarm Optimization with constriction coefficient is used as the optimization algorithm. The study covers multi-dimensional building design aims through a single-objective optimization approach where multi objectives are represented in a ε-Constraint penalty approach. The primary objective is taken as minimization of building global costs due to changes in design variables therefore it includes minimization of costs occur due to operational energy and water consumption together with ownership costs of building materials and building systems. Moreover, a set of penalty functions including equipment capacity, user comfort, CO2 emissions and renewable system payback period are added to the main objective function in the form of constraints to restrict the solution region to user-set design target. Consequently, multi-objective design aims are translated into a single-objective where the penalty functions acts as secondary objectives. The performance of the proposed optimization methodology was evaluated through a case study implementation where different design scenarios were created, optimized and analysed. A hypothetical base-case office building was defined. Three cities located in Turkey namely Istanbul, Ankara and Antalya were selected as building locations. Therefore, the performance of the methodology in different climatic conditions was investigated. An equipment database consists of actual building materials and system equipment commonly used in Turkish construction sector was prepared. In addition, technical and financial data necessary for objective function calculation were collected from the market. The results of the case studies showed that application of the proposed methodology achieved giving climate-appropriate design recommendations, which resulted in major cost reductions and energy savings. One of the most important contributing factors of this thesis is introducing an integrative method where building architectural elements, HVAC system equipment and renewable systems are simultaneously investigated and optimized while interactions between building and systems are being dynamically captured. Moreover, this research is distinctive from previous studies because it makes possible investigating actual market products as energy efficiency design options through its proposed database application and a sub-program that connect optimization engine with the data library. Therefore, application of the methodology can provide support on real-world building design projects and can prevent a mismatch between the optimization recommendations and the available market solutions. Furthermore, another contributing merit of this research is that it achieves formulating competing building design aims in a single objective function, which can still capture multi-dimensions of building design challenge. Global costs are minimized while energy savings are achieved, CO2-equivalent emission is reduced, right-sized equipment are selected, thermal comfort is provided to users and target payback periods of investments are assured. To conclude, the proposed methodology links building energy performance requirements to financial and environmental targets and it provides a promising structure for addressing real life building design challenges through fast and efficient optimization techniques

Influence of Uncertainty in User Behaviors on the Simulation-Based Building Energy Optimization Process and Robust Decision-Making

Computer-based simulations have been widely used to predict building performances. Building energy simulation tools are generally used to perform parametric studies. However, the building is a complex system with a great number of variables. This leads to a very high computational cost. Therefore, using a building optimization algorithm coupled with an energy simulation tool is a more promising solution. In this study, EnergyPlus is connected to a genetic algorithm that uses a probabilistic search technique based on evolutionary principles. Various sources of uncertainty exist in simulation-based building optimization problems. This study aims to investigate the influence of occupant behavior-related input variables on the optimization process. To integrate the uncertainty into the optimization process, a stochastic approach using the Latin hypercube sampling (LHS) method is employed. The varying input variables are defined by the LHS method, and each sampling run generates 14 samples. Five optimization parameters are used, and the recommendations for parameter settings of each parameter are generated as the optimization result. It is important to provide a decision maker with a decision-making framework to support robust decision-making from the generated recommendations. A clear or relatively clear tendency of recommendations toward a particular parameter setting is observed for three parameters. For these three parameters, the frequency of recommendation is identified to be a good indicator for the robustness of the most recommended setting. The test of proportion is performed to investigate the statistical significance between parameter settings. For the other two parameters, recommendations are comparatively evenly distributed among parameter settings, and the statistical significance is not shown. In this case, the Hurwicz decision rule is utilized to select an optimal solution. This dissertation contributes to the field of building optimization as it proposes a method to integrate uncertainty in input variables and shows the method generates reliable results. Computational time is reduced by using the LHS method compared to the case of using a random sampling method. While this study does not include all potential input variables with uncertainties, it provides significant insight into the role of input variables with uncertainty in the building optimization process.PHDArchitectureUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135836/1/nuri_1.pd

Qualitative and quantitative optimization of skylights : a comprehensive and inclusive analysis of skylight sizes for an office while providing enough daylight, avoiding glare and saving energy

While windows connect inside to outside, daylight entering through windows is a key element in architectural design. Although electrical lighting is able to replace daylight as an essential lighting requirement, daylight has qualitative and quantitative aspects that distinguish it from its competitor, electrical lighting. One of the most unique characteristics of daylight is its variability in time, including different qualities of daylighting from sunset to sunrise, and from equinox to solstice. In addition, by regulating a circadian rhythm and hormone secretion, daylight impacts the physiological and psychological well-being of human beings. Moreover, daylight through windows carries information that flows from outside to inside and makes occupants aware of the outside world. While availability of daylight has been praised in building design, uneven distribution of daylight, reflective surfaces and excessive daylight may cause glare issues and visual discomfort which need to be avoided in daylight design. Beyond all the qualitative aspects of daylight, daylight, as a free resource, is able to illuminate the space and replace electrical lighting and lower electricity utility bills. This quantitative aspect of daylight has been the center of attention among researchers, designers and builders, as lowering CO₂ emissions and environmental design have gained momentum in the building industry. Different stakeholders have various interests in qualitative and quantitative aspects of daylight, which eventually shape the design context. The interests of different stakeholders, including owners, environmentalists and occupants, may merge or conflict in different projects, which shows that daylight quality and quantity may have different weights, depending on the context of the project at hand. This dissertation aims to provide an algorithmic platform to consider a context for skylight design by including all the interests of different stakeholders while either scaling importance of the different interests or requiring minimum qualities and performance targets. This dissertation proposes different methodological approaches for its platform to include both qualitative and quantitative aspects in designing skylights for a one-storey office building in different climates. Three different approaches are proposed in this dissertation, encompassing unconstrained optimization, constrained optimization and monetary metrics. In the unconstrained optimization approach, the algorithmic platform has been developed to implement Parametric Analysis (PA) and Gradient Descent (GD) methods in order to optimize Skylight to Floor area Ratio (SFR) while saving energy consumption, as a quantitative aspect of daylight, and improving daylighting quality by providing sufficient daylight without causing glare discomfort. This platform was built as an Inclusive Integrative Algorithm (IIA) to weight different qualitative and quantitative aspects of daylight. The algorithm is able to perform single or multi-objective optimization by either applying GD or PA. In this approach, a single-objective optimization, considering only energy efficiency, showed that the optimal SFR was 6% in the examined climates of Austin, Chicago and San Francisco, for 300 lux lighting level and Lighting Power Density of 0.8 watt/sqft. The unconstrained optimization approach implemented a weighting system for an aggregated metric, including Mean Daylight (MD) and imperceptible Daylight Glare Probability (iDGP) and Ratio of Energy Saving (RES), which resulted in a SFR of 11% as the inclusive optimal solution for all the examined climates. In addition to the discussion of inclusive optimization considering both daylight and energy performance and scaling their importance, this dissertation initiated the use of GD for the unconstrained optimization in single and multi-objective optimization. The result showed that GD is considerably faster than the traditional method, PA, while predicting the optimal solution with higher resolution. For example, GD resulted in 6.22% SFR for the San Francisco climate as an energy efficient optimal solution by only 9 iterations. However, PA required 10,000 iterations to find the optimal solution with the same resolution. Thus, GD has shown a promising result for the future of multi-objective optimization in building design. In addition to the unconstrained optimization, this dissertation applied the second approach, constrained optimization, by imposing different thresholds for two sets of metrics, including daylight availability and glare. Where Useful Daylight Illuminance (UDI) and spatial Daylight Autonomy (sDA) of 100% were used, the inclusive optimal SFRs were 9-10%, 8-10% and 9% for the climates of San Francisco, Austin and Chicago, respectively. For the other set of daylight metrics, MD of 50% and Mean Daylight Glare Probability (mDGP) of 35% were used, which resulted in optimal solutions of 7-14%, 7-11% and 8-13% SFR for San Francisco, Austin and Chicago, respectively. Therefore, multi-objective optimization considering both daylight and energy performance resulted in different inclusive optimal solutions to energy optimization alone. The study also concludes that optimal solutions depend on applied metrics and daylight thresholds. For the third approach this research investigated the monetary gains from energy efficiency and increased productivity. Assuming that productivity does not occur in spaces with poor daylight performance, inclusive optimal solutions will be the scenarios that most probably boost productivity. The study indicated that the energy cost saving is always negligible compared to the monetary gains from minimum increased productivity (1%). This conclusion may influence an owner’s perspective toward the quality of daylight performance and its resultant productivity increase. Although the proposed algorithm (IIA) has been used to perform multi-objective optimization for skylight design, this platform can be used in the design process to optimize any fenestration, including widows, based on daylight availability, glare and energy factors. GD as one of the contributions of this dissertation is a faster and more accurate method which can facilitate the application of multi-objective optimization for daylight analysis in the early stage of design.Architectur