Community-scale Energy Research Articles

Advancing California's microgrid communities through anticipatory energy resilience

Given the uncertainty around climate change and the need to design systems that anticipate future needs, risks, and costs or values related to resilience, the current rules-based regulatory and policy frameworks designed for the centralized system of large-scale energy generation and delivery may not be ‘fit for purpose' for smaller scale local installations centered on community microgrids. This research examines regulatory challenges and potential impediments to implementing a multi-customer community-based microgrid in California through discussion of lessons learned in current pilot projects supported in part by initiatives of the California Energy Commission's Electric Program Investment Charge (EPIC). The extent to which regulation has the flexibility to anticipate future needs and risks and support experimentation is evaluated in light of the state's complex and evolving energy system requirements. To illustrate challenges, two case studies of EPIC-supported projects are included. Multiple uncertainties, including future impacts of climate change, energy demands, and advances in technology, highlight the potential need to rethink best approaches to energy regulation. Principles drawn from Resilience Thinking and Anticipatory Regulation are discussed for their potential value in supporting development of new models for community-scale energy production, distribution, and use. Drawing on the experiences of the pilot projects, suggested principles to guide a new regulatory regime specific to microgrids are proposed.

An efficient energy management framework for residential communities based on demand pattern clustering

Intelligently managing energy production and consumption on community basis can enhance the demand side energy efficiency. The fact that the number of energy resources (especially controllable appliances) could be large imposes non-trivial computational burden to community-scale energy management. This paper proposes a community energy management framework that coordinately manages the operations of different kinds of energy resources in a community (i.e., photovoltaic solar power sources, battery energy storage systems (BESSs), and controllable appliances from different households) to minimize the community’s energy cost while sufficiently considering the peak-to-average ratio (PAR) of the community’s load and the occupants’ satisfaction. The proposed framework is with a two-stage design: in the day-ahead stage, multiple “virtual appliances” are formed, where each virtual appliance represents a group of appliances that have similar operational patterns. Based on this, a day-ahead scheduling model is formulated to determine the optimal operation plans of the virtual appliances and other energy resources. The virtual appliances’ schedules are then mapped to actual individual appliances through the developed mapping algorithms. In the real-time operation stage, a receding horizon-based energy management model is developed for correcting the BESS’s operation subjected to the day-ahead plan and real-time solar power output and uncontrollable load. Extensive simulation is conducted to validate the proposed framework based on a real-world dataset.

Developing and tuning a community scale energy model for a disadvantaged community

This work describes the development of a community-scale energy model for a mixed-use low-income community located in Huntington Beach, CA. An accurate community-scale energy model is useful for evaluating the use of limited capital resources used to invest in clean energy technologies. This work lays out the process of developing such a model while relying primarily on publicly available data and highlighting critical partnerships necessary for model development success. The primary contribution of this work is the demonstration of the process used to develop an accurate energy model for a disadvantaged community when minimal building and energy use data is available. The heart of the model is the physics-based community scale energy modeling platform URBANopt. Using a bottom-up load modeling approach, energy simulated energy use falls within 3% or less of aggregate annual utility data, and within 10% or less aggregate monthly utility data. The demonstrated model development and tuning process can be used by others to characterize other atypical communities, which may differ significantly from prototypical models.

Exploring Grassroots Renewable Energy Transitions: Developing a Community-Scale Energy Model

Decarbonizing energy systems through the integration of decentralized renewable energy generators creates opportunities for community-scale actors to participate in energy system decision-making. However, typical modelling approaches exclude community stakeholders, causing a loss of local knowledge. This exclusion is problematic for Indigenous peoples in so-called Canada where the natural resource industry harms their land and communities. The Exploring Grassroots Renewable Energy Transitions (EGRET) platform introduced in this work presents an alternative to typical energy system modelling because it facilitates community participation throughout the model development and application process. This platform was developed in partnership with a local First Nation's energy specialist to assess whether solar panels could increase community energy sovereignty. The platform's user interface, visualization suite, and high-speed machine learning models make energy system modelling accessible to community members through interactive workshops. In the future, the EGRET approach could be generalized for stakeholder-led renewable energy exploration in other community settings.

Analysis of Building Archetypes for Optimising New Photovoltaic Energy Facilities: A Case Study

Fuel poverty rates are high in southern Europe, particularly in social housing, despite the enormous potential for capturing solar power inherent in the roofs of apartment buildings. The in situ generation, distribution and consumption of photovoltaic energy carry obvious advantages including vastly improved efficiency attendant upon the reduction in distribution-related losses and costs, and the energy empowerment afforded lower income communities. The primary drawback is the imbalance between photovoltaic production patterns and users’ actual needs (peak consumption vs. peak generation). That mismatch is difficult to reconcile without resorting to energy storage or net metering, both of which entail grid involvement and greater management complexity. The present study introduces a methodology for analysing residential archetypes to determine the values of the parameters essential to optimising photovoltaic energy production and use. The aim is to determine where excess generation can be shared with other users in the vicinity and optimally pool residential rooftop facilities to meet community-scale energy demand, ultimately enhancing such disadvantaged neighbourhoods’ self-sufficiency. The case study discussed defines archetypes for just such a neighbourhood in Madrid, Spain. The solar energy production potential of the example is promising for its application in large southern European cities, with self-sufficiency rates obtained ranging from 15% to 25% and self-consumption rates from 61% to 80%.

A mutually beneficial approach to electricity network pricing in the presence of large amounts of solar power and community-scale energy storage

Electricity distribution networks that contain large photovoltaic solar systems can experience power flows between customers. These may create both technical and socio-economic challenges. This paper establishes how these challenges can be addressed through the combined deployment of Community-scale Energy Storage (CES) and local network tariffs. Our study simulates the operation of a CES under a range of local network tariff models, using current Australian electricity prices and current network prices as a reference. We assess the financial outcomes for solar and non-solar owning customers and the distribution network operator. We find that tariff settings exist that create mutual benefits for all stakeholders. Such tariffs all apply a discount of greater than 50% to energy flows within the local network, relative to regular distribution network tariffs. The policy implication of these findings is that the, historically contentious, issue of network tariff reform in the presence of local solar power generation can be resolved with a mutually beneficial arrangement of local network tariffs and CES. Furthermore, the challenge of setting appropriate tariffs is eased through clear and intuitive conditions on local network tariff pricing.

A modelling approach and a case study to answer the question: What does it take to retrofit a community to net-zero energy?

In order to have an immediate impact on building energy-related emissions, existing buildings need to be addressed. To inform policy and technical decisions, detailed mathematical models are required which can explore numerous building retrofit solutions and their energy and emissions performance. This paper describes the adaptation of a hybrid statistical and engineering-based model for residential building stock in Canada to analyze community-scale energy retrofits. The modelling domain includes both envelope and mechanical retrofits, as well as district renewable energy systems. This model is then applied to a case study of converting a community of fifty 1980’s vintage single-detached homes to achieve net-zero energy. The case study demonstrated that deep envelope retrofits and fuel switching from natural gas to electric heat pump systems reduce community energy demand by 69%. Saturating available roof area with photovoltaics was able to achieve net-zero balance. By considering net-zero at the community-scale, individual buildings that did not achieve net-zero were offset by net-exporting neighbours. Annual community emissions of the retrofit reduced emissions by 95%. The analysis also highlights the significant impact on electrical infrastructure due to solar generation and energy demand mismatch.

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

Community-scale energy planning entails concerted efforts for realizing localized energy and emission plans geared towards achieving energy sustainable communities. This is largely under-investigated for the developing world together with exploring the synergies between community planning and energy planning strategies. This study performs community-scale energy planning from 2015 to 2040 for Onyen-okpon and Giere, two rural communities in Nigeria. Utilizing the hybrid integration approach with the Low Emissions Analysis Platform model, the study investigates the implications for transforming rural communities to sustainable renewable based energy supply and quantitatively analyses the strategy integration based on four scenarios: the reference scenario and three composite demand side management scenarios. The results show that the integration of strategies has clear benefits with the composite scenarios availing reduced energy use with significant reductions in energy demand and greenhouse gases emissions. The third demand side management scenario provides the best performance among the composite scenarios in terms of demand and emissions reductions, energy conservation, and overall reduction in energy expenditures. The integration approach provides useful insights for design and implementation of localised energy policies across different sectors in the community where productive uses are taken into account for clean and sustainable economic production. This avails the opportunity to concurrently define and reach multiple targets, goals and impacts in reducing emissions while ensuring energy access and productive services.

Techno-economic assessment of biomass gasification-based mini-grids for productive energy applications: The case of rural India

As the costs of solar PV continuously decrease and pollution legislation imposes less burning of agricultural residues, decentralized renewable energy is increasingly affordable for providing electricity to one billion people lacking access to a power grid. This paper presents a techno-economic feasibility case study of biomass gasification in off-grid and grid-connected mini-grids for community-scale energy application in rural Uttar Pradesh, India. Energy demand data was collected through surveys in a village with irrigation and agro-processing loads and off-grid households and used to construct a seasonal load profile based on statistical methods. This was used to simulate single-source and hybrid mini-grids based on solar PV, biomass gasification and diesel generation using HOMER Pro. Hybrid PV-biomass or PV-diesel systems were found to offer the highest reliability for off-grid power at the lowest cost. Single-source PV was cheaper than biomass gasification, though the cost of electricity is highly sensitive to biomass supply and gasifier maintenance. Both renewable options were around half the cost of diesel generation. The findings held across grid-connected systems with weak, moderate and strong reliability of grid supply. This suggests that biomass gasification-based mini-grids are not cost-competitive with PV unless the two generation sources are combined in a hybrid system, though they require operational testing prior to implementation.

The Combustion Characteristics of Biomass Stove with Preheated-Air by Porous Medium

This study aims to characterize the combustion of the biomass stove filled with a porous medium for air pre-heating to enhance the biomass decomposition. An equivalence ratio, one of the important parameters for the combustion, was investigated to evaluate the completeness of the combustion process. Thermal efficiency was used as the indicator to reflect the performance of the biomass stove. Which was divided into two parts namely the pre-combustion (PCC) and the combustion chamber (CC). The CC volume was 28 liters for fuel storage, and it was insulated for reducing heat loss. The stove was made of 2 mm steel plate and the porous medium was a round-shape ceramic with a diameter of 10 mm. The boiling test was performed using corn cobs as a single solid fuel to boil water in the vessel. The temperature of the stove was measured using a thermocouple Type-K and a data recorder was used to collect the experimental data. The airflow rate was determined using an airflow meter and the fuel consumption rate was calculated using the aforementioned experimental data. Finally, the designed biomass stove will be demonstrated at the community to simultaneously enhance the community-scale energy and reduce the environmental issues.

Understanding citizen perspectives on open urban energy data through the development and testing of a community energy feedback system

With the rise of advanced and affordable sensors offering continuous monitoring of city infrastructure, cities are increasingly seeking to become more ‘smart’ and are adopting data-driven approaches to help meet sustainability goals. In the area of building energy efficiency, closely coupled with this effort is the prevalence of building energy benchmarking policies, which require public disclosure of vast new quantities of building-level energy data at urban scales (i.e., open urban energy data). While existing research efforts have focused on the potential of this data to transform energy efficiency markets and investments in the real estate sector, little research has been dedicated to assessing this information’s value to the general public. Given that achieving energy reductions in the built environment will require not only energy efficiency investments, but also greater awareness, engagement, and action from ordinary citizens, we study the potential of open urban energy data in providing citizen benefits. Energy-cyber-physical systems offer a pertinent framework to link data from the virtual world to citizens’ physical reality in order to improve their understanding and decision making. Adopting an energy-cyber-physical system perspective, we aim to connect open urban energy data to citizens through the development and evaluation of a novel community-scale energy feedback system. This mobile cyber-physical system transforms building-level electricity consumption and production data across Georgia Tech’s campus into a mobile application consisting of three features: spatial feedback, energy supply feedback, and energy consumption feedback. Augmented-reality visualization elements are integrated into the system, providing Georgia Tech community members a direct link between their experienced physical environment and data stored in the virtual world. Applying a user-centered design approach, prospective users evaluate the system via thinking aloud sessions and user surveys to assess understandings and perceptions of open urban energy data for the Georgia Tech campus. The results contribute to literature seeking to create energy feedback systems at the community-scale and expand research investigating citizen reactions to and opinions of open urban energy data. This research is an integral step to further engagement and participation from the public to help achieve a sustainable and citizen-valued energy future.

Designing community-scale energy feedback

Energy systems are evolving from large scale, centralized networks to decentralized, community-scale energy systems, within which citizens are expected to have much more central and proactive roles. Knowing that levels of citizen involvement can heavily influence the success and efficiency of community energy projects, research is exploring means to boost community-scale citizen participation within energy systems. Concurrently, building-level energy data is becoming increasingly available city-wide, which has potential to increase citizen learning and engagement with the energy performance in their community. However, research on how to make this data accessible, understandable, and useful to citizens is limited. This study applies findings from energy feedback literature to propose the design of a novel energy feedback system at the community scale. We demonstrate the principles that inform the design of our community energy feedback system in presenting the front- and back-end of a prototype system. We conclude with a discussion of this system’s limitations, plans for future research, and potential to improve citizen understanding, engagement, and behaviors with energy systems in order to achieve community-scale, and, by extension, urban scale, energy goals.

A modelling tool selection process for planning of community scale energy systems including storage and demand side management

Storage and Demand Side Management (DSM) are key in integrating renewable energy into community energy systems. There are many modelling tools which support design of such systems. In order to select an appropriate tool it is essential to understand tool capabilities and assess how these match requirements for a specific situation. The aim of this paper is to provide a process to be used to make such a selection consisting of: (i) a tool capability categorisation, (ii) a stepwise tool selection process.Capabilities of 13 tools (screened from 51) for community scale were categorised covering: input data characteristics; supply technologies; design optimisation; available outputs; controls and DSM; storage; and practical considerations.A stepwise selection process is defined, adapted from software engineering, in which tools are scored based on ‘essential’, ‘desirable’, or ‘not applicable’ technical capabilities for the specific situation. Tools without essential capabilities are eliminated. Technical scores and practical considerations are then used to select the tool. The process is demonstrated for a simple case study.The future applicability of the selection process is discussed. Findings from the capability categorisation process are highlighted including gaps to be addressed and future trends in modelling of such systems.

Typology of future clean energy communities: An exploratory structure, opportunities, and challenges

This paper considers the potential significance of ‘clean energy communities’ (CECs) in the transformation of the present socio-technical regimes underlying our centralized energy systems, towards a more distributed and decentralized future. It explains the centralized, distributed, and decentralized sub-structures, embedded in current energy systems and energy markets. We analyze long-term dynamics and possible pathways of this transition, and the co-evolution of energy systems and communities, using an exploratory structure, drawing on insights from transition theories, innovation studies, and social network concepts. This includes analysis of the various forms of CECs – including virtual power plants, peer-to-peer trading, microgrids, and community-scale energy projects – emerging in a number of developed and developing jurisdictions, including Australia. This analysis suggests that low-carbon transition pathways will be varied, driven by social, technological, and organizational contexts, and shaped by institutional change processes, and interaction with the existing regime and incumbent actors. Social and technological entrepreneurs, and utilities, operating within an environment increasingly defined by energy consumers, industry groups, and policy actors, will need to be adaptable and innovative in choosing strategic directions, associated investment decisions, establishing appropriate alliances, and acquiring resources, to meet their goals in this low-carbon energy transition.

Adaptation and validation of an existing bottom-up model for simulating temporal and inter-dwelling variations of residential appliance and lighting demands

The design and analysis of community-scale energy systems and incentives is a non-trivial task. The challenge of such undertakings is the well-documented uncertainty of building occupant behaviours. This is especially true in the residential sector, where occupants are given more freedom of activity compared to work environments. Further complicating matters is the dearth of available measured data. Building performance simulation tools are one approach to community energy analysis, however such tools often lack realistic models for occupant-driven demands, such as appliance and lighting (AL) loads. For community-scale analysis, such AL models must also be able to capture the temporal and inter-dwelling variation to achieve realistic estimates of aggregate electrical demand. This work adapts the existing Centre for Renewable Energy Systems Technology (CREST) residential energy model to simulate Canadian residential AL demands. The focus of the analysis is to determine if the daily, seasonal, and inter-dwelling variation of AL demands estimated by the CREST model is realistic. An in-sample validation is conducted on the model using 22 high-resolution measured AL demand profiles from dwellings located in Ottawa, Canada. The adapted CREST model is shown to broadly capture the variation of AL demand variations observed in the measured data, however seasonal variation in daily AL demand behaviour was found to be under-estimated by the model. The average and variance of daily load factors was found to be similar between measured and modelled. The model was found to under-predict the daily coincidence factors of aggregated demands, although the variance of coincident factors was shown to be similar between measured and modelled. A stochastic baseload input developed for this work was found to improve estimates of the magnitude and variation of both baseload and peak demands.

Adaptation planning of community energy systems to climatic change over Canada

Adaptation planning of energy systems to climatic change is highly complicated due to complex interactions among various adaptation responses, and among energy-related activities. These complexities may be further compounded as a result of the presence of interval-format uncertainty associated with energy systems management and climate change adaptation responses. This study is to develop an inexact community-scale energy system adaptation model (ICEAM) for supporting adaptation planning of community-scale energy systems under uncertainty. The objective entails the tasks including: (1) investigation of adaptation responses of the energy sector to climate change, (2) uncertainty analysis, and (3) development of ICEAM and apply it to the City of Waterloo, Canada. The results indicate that, to adapt to a changing climate by the City of Waterloo, more electricity and gasoline and less natural gas would be imported. The results also suggest that the ICEAM has an advantage of the planning adaptation response of energy activities, but also addressing the uncertainty existing in energy management systems and regional climate prediction.

Simulation of cogeneration within the concept of smart energy networks

Recently tremendous research effort has been made to cope with the challenges in providing a sustainable energy solution for achieving economic and environmental goals. The concept of smart energy networks (SEN) goes beyond the electricity-based smart grid (SG) environment. The availability of new technologies for utilizing renewables such as solar, wind, geothermal and biomass and possibility of integrating natural gas and heating/cooling pipelines into the electricity grid network have provided a base for reducing the carbon footprint of fossil fuels and to produce a better energy security in a SEN environment. Especially, cogeneration systems in SEN can provide a promising solution for effectively supplying energy from a variety of combine heat and power generators to district consumers in an urban environment. In this study, a community-scale energy network is modeled and simulated using TRNSYS (Transient System Simulation Software) to investigate the impacts of the selected cogeneration systems on real-time critical parameters of SEN. The simulation results show that, through analyzing the key factors, the cogeneration model can be technically and economically feasible in SEN environment. The model follows effectively the heat-to-power ratios of the individual components and meets the energy demand with less excess electricity supply. The model describes a high renewable contribution ratio of 23.5% and gives a high overall efficiency which is about 12.6% higher than that of the separate heat and power generation system, resulting in a low emission level which is 34.2% lower than that of the separate heat and power generation.

Optimum design of district heating: Application of a novel methodology for improved design of community scale integrated energy systems

The purpose of this paper is to present a new tool for design of integrated energy systems. The initial choice of the energy system components and the way they should interact is a crucial decision which the outcome of the design heavily relies on. Use of a physical law (instead of engineering judgment) as the basis of the decision making is the main advantage of the proposed approach over conventional approaches for design of community scale energy systems. The methodology has been implemented for design of a district heating system for an existing district in arid region of Iran. The optimum level of interaction between the energy system components has been identified by employing an optimization algorithm seeking to minimize the overall cost of the energy system. Some of the relative merits of the optimum design comparing to the present energy system are 17% increase of the exergy efficiency, 10.8% reduction in the amount of CO2 production per capita and 2% reduction in overall energy related costs.

A modeling approach for investigating climate change impacts on renewable energy utilization

SUMMARY In this study, an integrated community-scale energy model (ICEM) was developed for supporting renewable energy management (REM) systems planning with the consideration of changing climatic conditions. Through quantitatively reflecting interactive relationships among various renewable energy resources under climate change, not only the impacts of climate change on each individual renewable energy but also the combined effects on power-generation sector from renewable energy resources could be incorporated within a general modeling framework. Also, discrete probability levels associated with various climate change impacts on the REM system could be generated. Moreover, the ICEM could facilitate capacity–expansion planning for energy-production facilities within a multi-period and multi-option context in order to reduce energy-shortage risks under a number of climate change scenarios. The generated solutions can be used for examining various decision options that are associated with different probability levels when availabilities of renewable energy resources are affected by the changing climatic conditions. A series of probability levels of hydropower-, wind- and solar-energy availabilities can be integrated into the optimization process. The developed method has been applied to a case of long-term REM planning for three communities. The generated solutions can provide desired energy resource/service allocation and capacity–expansion plans with a minimized system cost, a maximized system reliability and a maximized energy security. Tradeoffs between system costs, renewable energy availabilities and energy-shortage risks can also be tackled with the consideration of climate change, which would have both positive and negative impacts on the system cost, energy supply and greenhouse-gas emission. Copyright © 2011 John Wiley & Sons, Ltd.

STUDY ON NON-ENERGY BENEFITS (NEB) BY AREA-WIDE ENERGY UTILIZATION (PART 2)

Toward low-carbon society, much higher use of renewable energy and unused energy should be promoted. For this purpose, area-wide energy utilization is expected to play an important role in commercial and residential sectors. Along with the compact city policy, area-wide energy utilization in rather wide scale such as in a community and in a city should be strategically planned and implemented. Among various carbon reduction measures, those of higher reduction potential and higher cost-benefit ratio should be prioritized. This paper describes an appropriate way of determining the priority by use of marginal abatement cost (MAC), taking the following viewpoints into consideration.; 1) Setting payback-periods based on functional lifetime of each reduction measure 2) Qualifying non-energy benefits (NEB) to be deducted from the MAC of each reduction measureA case study for a community-scale energy utilization by use of incineration waste heat is undertaken to validate the above concept.