Building Microgrid Research Articles

Cost-effective and optimal pathways to selecting building microgrid components – The resilient, reliable, and flexible energy system under changing climate conditions

Amidst the changing climate conditions, electricity retailers-led demand response programs and the emergency backup power for possible grid outages are often discussed in isolation, although several underlying linkages exist, which are important for how the existing building stock evolves with the energy transition. This study navigates through the linkages while investigating the levelized cost of electricity (LCOE)-based building microgrid components and undertakes a comparative analysis of energy optimisation models with and without emergency diesel generators. It also examines the building energy system’s resilience, reliability, and flexibility by using OpenStudio and HOMER Grid to develop energy simulation and optimisation models and other probabilistic models for a chosen building archetype in Sydney, Australia. This study finds that excluding the emergency diesel generator will require a larger battery storage system, increasing LCOE from A$0.17/kWh to A$0.20/kWh across climate scenarios. However, the larger battery storage system increases the probability of surviving outages (> 4 h) by around 10 % across climate scenarios. The LCOE increases up to 45 % for outages up to 8 h across climate scenarios and up to 85 % for outages up to 12 h. Additionally, for the building archetype, the probability of grid purchase (below the current average) is 0.78 in 2030, which drops to 0.55 in 2050. The probability of grid sales (above the current average) also drops from 0.69 to 0.46. Thus, while the narratives around the building energy system’s flexibility overstate the electricity exchange between the building and the grid, this study finds that increasing the on-site production utilisation rate and larger battery throughput contributes to demand response application and flexibility.

Review of energy management systems and optimization methods for hydrogen‐based hybrid building microgrids

AbstractRenewable energy‐based microgrids (MGs) strongly depend on the implementation of energy storage technologies to optimize their functionality. Traditionally, electrochemical batteries have been the predominant means of energy storage. However, technological advancements have led to the recognition of hydrogen as a promising solution to address the long‐term energy requirements of microgrid systems. This study conducted a comprehensive literature review aimed at analysing and synthesizing the principal optimization and control methodologies employed in hydrogen‐based microgrids within the context of building microgrid infrastructures. A comparative assessment was conducted to evaluate the merits and disadvantages of the different approaches. The optimization techniques for energy management are categorized based on their predictability, deployment feasibility, and computational complexity. In addition, the proposed ranking system facilitates an understanding of its suitability for diverse applications. This review encompasses deterministic, stochastic, and cutting‐edge methodologies, such as machine learning‐based approaches, and compares and discusses their respective merits. The key outcome of this research is the classification of various energy management strategy (EMS) methodologies for hydrogen‐based MG, along with a mechanism to identify which methodologies will be suitable under what conditions. Finally, a detailed examination of the advantages and disadvantages of various strategies for controlling and optimizing hybrid microgrid systems with an emphasis on hydrogen utilization is provided.

Multi-Layer Model Predictive Optimization of Energy Efficient Building Microgrids

Multi-Layer Model Predictive Optimization of Energy Efficient Building Microgrids

Techno-economic analysis of green hydrogen as an energy-storage medium for commercial buildings

Abstract Green-hydrogen production is vital in mitigating carbon emissions and is being adopted globally. In its transition to a more diverse energy mix with a bigger share for renewable energy, United Arab Emirates (UAE) has committed to investing billions of dollars in the production of green hydrogen. This study presents the results of the techno-economic assessment of a green-hydrogen-based commercial-building microgrid design in the UAE. The microgrid has been designed based on the building load demand, green-hydrogen production potential utilizing solar photovoltaic (PV) energy and discrete stack reversible fuel cell electricity generation during non-PV hours. Given the current market conditions and the hot humid climate of the UAE, a performance analysis is derived to evaluate the technical and economic feasibility of this microgrid. The study aims at maximizing both the building microgrid’s independence from the main grid and its renewable fraction. Simulation results indicate that the designed system is capable of meeting three-quarters of its load demand independently from the main grid and is supported by a 78% renewable-energy fraction. The economic analysis demonstrates a 3.117-$/kg levelized cost of hydrogen production and a 0.248-$/kWh levelized cost for storing hydrogen as electricity. Additionally, the levelized cost of system energy was found to be less than the current utility costs in the UAE. Sensitivity analysis shows the significant impact of the capital cost and discount rate on the levelized cost of hydrogen generation and storage.

Optimization of building microgrid energy system based on virtual energy storage

With the continuous development of building microgrids, it is crucial to explore and study the energy-saving potential of buildings to resolve energy shortages and environmental protection problems. Therefore, to realize the efficient and economical operation of a building microgrid, a new multi-objective optimization method is proposed for the planning and operation of building microgrid considering virtual energy storage. First, virtual energy storage model of the building microgrid is established based on the heat storage characteristics of the building itself. Second, a multi-objective optimization model of the building microgrid considering virtual energy storage is constructed by considering the investment cost and the comprehensive operation benefits as the objectives as well as the constraints of cooling, heating, power balance. Finally, based on the original gray wolf swarm algorithm, the niche processing mechanism and gray weighted correlation method are introduced to optimize the configuration and operation of the building microgrid. For a typical building microgrid in summer and winter, the simulation results reveal that the proposed method improves the overall economy of planning the building microgrid system and its operation and user experience via virtual energy charge and discharge management.

Hierarchical Model Predictive Control to Coordinate a Vehicle-to-Grid System Coupled to Building Microgrids

Aiming to take full advantage of Electric Vehicles’ (EVs) batteries, this paper proposes a two-level hierarchical model predictive controller coupled with an innovative charging-discharging scheduler for EVs in Building Microgrids (BMGs). This paper provides a complete framework for the design of this control structure and analyses its performance regarding the state of charge of the EVs at departure time, the self-consumption rate, and the coverage rate, considering a residential BMG equipped with photovoltaic panels and static Li-ion batteries. The results and performance of the proposed control architecture are compared to two other solutions: a hierarchical predictive controller with no scheduler and a rule-based algorithm. A technological and economical study is also performed considering variables such as the dimension of the EV's park, the price of energy, the cost of maintenance, the possibility to discharge or not into the grid, and the execution time of the control architecture. The simulation results conducted in MATLAB Simulink demonstrated that the proposed control structure ensures the full charging of all vehicles at departure time while also improving the self-consumption rate of the BMG with a relatively low stress on the needed computation capacities, even when considering a large fleet of vehicles.

An Online Multi-Level Energy Management System for Commercial Building Microgrids With Multiple Generation and Storage Systems

This paper presents an online multi-level energy management system for local microgrids of commercial buildings that integrate roof-top photovoltaic sources, battery storage systems, utility grids, diesel generators, supercapacitors, and commercial buildings consisting of active orchestrated loads, to solve the uncertainty problem of sources and loads, while also optimizing the local microgrid operation cost of commercial buildings and the utilization rate of local renewable energy. The energy management system includes a long-term rolling optimization level, rule-based optimization level, and load demand optimization level. At the long-term rolling optimization level, an online rolling method of data restructuring is proposed, where measurement data, short-term prediction data, and day-ahead prediction data are reconstructed to reduce the uncertainty in photovoltaic source prediction and load demand prediction. Four methods are proposed for the energy management system and simulated in MATLAB/Simulink under three typical weather conditions, cloudy, sunny, and rainy. Simulation results show that the performance of Method 3 is closest to that of Method 4, whose data conditions are ideal; Method 3 reduces the operational cost of the commercial building microgrid and improves the utilization rate of photovoltaic sources, at the slight cost of non-critical load shedding.

Real-time multi-energy demand response for high-renewable buildings

Proactive demand response is a cost-effective approach to enhance energy flexibility of high-renewable urban energy systems. This paper proposes a two-stage real-time multi-energy demand response framework for a high-renewable building microgrid. In the first stage, a rolling horizon-based on-line scheduling model is formulated to economically optimize the building multi-energy converters and storages via a novel transactive pricing mechanism. In the second stage, the schedulable multi-energy flexibility for response devices is defined and evaluated via comprehensive information. After obtaining the stochastic optimal economical scheduling results in the first stage, the second stage performs a rule-based faster-time scale multi-energy allocation to offset the real-time mismatch energy due to supply–demand uncertainties. Case studies over a building microgrid are performed to validate the effective and superior performances of the proposed method on improvements of multi-energy economy and flexibility. Simulations results show that the system operating cost can be reduced by at most 36.9% with a higher operational flexibility.

Optimal integration of Vehicle to Building (V2B) and Building to Vehicle (B2V) technologies for commercial buildings

The advent of vehicle-to-building (V2B) technology provides an option to export electricity from the battery in electric vehicles (EVs) into buildings to assist in meeting building electric demands. However, there are some constraints on operating the EV fleets as flexible energy storage for buildings, including (i) stochastic and limited availability of EVs for building energy management; and (ii) necessity of bi-directional flow of EVs to operate in both V2B and building-to-vehicle (B2V) modes. Hence, these constraints and the changing degree of electric energy exchange provided by the different numbers of EVs could affect the building energy management system and the dynamic energy usage. The main objectives of this paper are to study the optimal configurations of the number of EVs interacting with a building by examining (1) the impact of different numbers of EVs on the daily electricity bills in a grid-connected commercial building microgrid, (2) the range of allowable EV numbers that enable optimal V2B capacity and reasonable charging loads for enhancing the cost-effectiveness of building energy management; (3) the role of different EV numbers in affecting the required stationary battery energy storage system (BESS) capacity in an isolated commercial building microgrid. It is observed that optimal building connected EV numbers are determined by EV driving patterns, seasonal solar output, building demand profiles, and the price of grid electricity. Thus, a scenario analysis is conducted to determine a range of suitable EV numbers in each scenario. Simulation results indicate that the optimal number of EVs varies greatly between summer and winter, as well as across different solar irradiance scenarios. In a case study where a building’s peak electric consumption is 30 kW and the PV system’s rated capacity is 50 kW, the allowable EV number, depending on the amount of available solar energy, varies from 8 to 29 in summer and changes from 2 to 15 in winter. These results could be used to suggest the optimal EV numbers based on the day-ahead solar energy forecasting, enabling day-ahead planning to either attract more vehicle owners to boost V2B capacity or to limit the number of integrated EVs in order to avoid a spike in total EV charging demand.

Technical and Economic Assessment of Battery Storage and Vehicle-to-Grid Systems in Building Microgrids

In an electrical microgrid, distributed renewable generation is one of the main tools used to achieve energy sustainability, cost efficiency and autonomy from the grid. However, reliance on intermittent power sources will lead to a mismatch between generation and demand, causing problems for microgrid management. Flexibility is key to reducing the mismatch and providing a stable operation. In such a context, demand response and energy storage systems are the main factors that contribute to flexibility in a microgrid. This paper provides an assessment of the technical and economic impacts of a microgrid at the building level, considering photovoltaic generation, battery energy storage and the use of electric vehicles in a vehicle-to-building system. The main novel contributions of this work are the quantification of system efficiencies and the provision of insights into the design and implementation of microgrids using real on-site data. Several tests were conducted using real on-site data to calculate the overall efficiencies of the different assets during their operation. An economic assessment was carried out to evaluate the potential benefits of coordinating battery storage with a vehicle-to-building system regarding the flexibility and cost-efficient operation of the microgrid. The results show that these two systems effectively increase the levels of self-consumption and available flexibility, but the usefulness of private electric vehicles in public buildings is constrained by the schedules and parking times of the users. Furthermore, economic benefits are highly dependent on the variability of tariffs and the costs of energy storage systems and their degradation, as well as the efficiency of the equipment used in the conversion chain.

Stochastic Energy Management Strategy of Smart Building Microgrid with Electric Vehicles and Wind-Solar Complementary Power Generation System

Stochastic Energy Management Strategy of Smart Building Microgrid with Electric Vehicles and Wind-Solar Complementary Power Generation System

Scenario-Based Stochastic Optimization for Energy and Flexibility Dispatch of a Microgrid

Energy storage is one of the most important components of microgrids with non-dispatchable generators and can offer both energy and flexibility services when the microgrid operates in grid-connected mode. This paper proposes a scenario-based stochastic optimization model that can be used to determine the energy and flexibility dispatch of a residential microgrid with solar and stationary battery systems. The objective of the model is to minimize the expected energy and peak power cost as well as the battery aging cost, while maximizing the expected revenue from flexibility. The formulated stochastic optimization problem is solved in rolling horizon with the uncertainty model being dynamically updated to consider the most recent forecast profiles for solar power and electricity demand. The benefits of the proposed approach were demonstrated by simulating the daily operation of a real building. The results showed that the estimated flexibility was successfully dispatched yielding an economic value of at least 7% of the operation cost of the building microgrid. The model can be used by flexibility providers to assess their flexibility and design a bidding strategy as well as by system operators to design incentives for flexibility providers.

Stochastic-Weighted Robust Optimization Based Bilayer Operation of a Multi-Energy Building Microgrid Considering Practical Thermal Loads and Battery Degradation

This paper discusses a bilayer coordinated operation scheme for the multi-energy building microgrid (MEBM) with comprehensive uncertainty sources. First, a building model considering the battery degradation, practical/detailed thermal loads, and various operating tasks of residential appliances is presented. Second, to alleviate the adverse effects from diverse uncertainties of electricity demands, cooling water temperature, outdoor temperature, occupants’ metabolism as well as solar irradiance, a bilayer model is applied which makes full utilization of historical data and manages the solution conservativeness. The first layer is the stochastic-weighted robust optimization-based day-ahead operation. It determines the dispatch of heterogeneous energy storage assets, multi-energy demand response, and on-off status of combined cooling, heat and power (CCHP) plants on an hourly basis for the entire day. The second layer sequentially finalizes the operation of the power-to-thermal conversion unit, CCHP plant, and electricity transactions between the MEBM and utility grid hourly with uncertainty realizations. Numerical case studies demonstrate the effectiveness of the proposed approach in obtaining the economic MEBM operation with the high comput-ational performance and immunizing against uncertainties.

Smart Thermostats for a Campus Microgrid: Demand Control and Improving Air Quality

Achieving nearly zero-energy buildings (nZEB) is one of the main objectives defined by the European Union for achieving carbon neutrality in buildings. nZEBs are heavily reliant on distributed renewable generation energy sources, which create new challenges associated with their inherent intermittency. To achieve nZEB levels, demand management plays an essential role to balance supply and demand. Since up to two-thirds of the total consumed energy in buildings is dispended for Heating, Ventilation and Air Conditioning (HVAC) operations, intelligent control of HVAC loads is of utmost importance. The present work aims to offer a solution to improve a building microgrids’ flexibility by shifting thermal loads and taking advantage of room thermal inertia. Innovation is present in using the internet of things to link several decentralized local microcontrollers with the microgrid and in the applicability of different control algorithms, such as the pre-emptive heating/cooling of a room. The developed solution relies on smart thermostats, which can be integrated into a building management system, or in a microgrid, and are capable of fulfilling the occupants’ need for comfort while complementing the building with needed power flexibility. The equipment is capable of controlling several HVAC systems to guarantee thermal and air quality comfort, as well as coordinate with a building/microgrid operator to reduce energy costs by shifting thermal loads and enacting demand control strategies. The smart thermostat uses an algorithm to calculate room inertia and to pre-emptively heat/cool a room to the desired temperature, avoiding peak hours, taking advantage of variable tariffs for electricity, or periods of solar generation surplus. The smart thermostat was integrated into a university campus microgrid and tested in live classrooms. Since the work was developed during the COVID-19 pandemic, special attention was given to the air quality features. Results show that smart HVAC control is a viable way to provide occupant comfort, as well as contribute to the integration of renewable generation and increase energy efficiency in buildings and microgrids.

Study on the performance and economy of the building-integrated micro-grid considering photovoltaic and pumped storage: a case study in Foshan

Abstract Micro-grid is a promising technology for the energy reform in urban areas. This paper takes a building as a case study to construct a micro-grid system, which includes rooftop photovoltaic and pumped storage systems according to the characteristics of the building. Moreover, the cost–benefit analysis of the commercial building micro-grid system is also performed to obtain an in-depth understanding of the feasibility of the project. According to the characteristics of power load of commercial buildings, the working characteristics of the developed micro-grid and the mathematical model of optimal operation of micro-grid in commercial buildings is constructed. The calculation results from the mathematical model show that the dynamic payback period (PBP) of the micro-grid system of commercial buildings is 4.74 years, while the dynamic PBP of the micro-grid system of battery energy storage is 7.95 years. Therefore, the micro-grid system pumped storage has a much better economy than that with battery energy storage.

Optimization of electrical energy waste in house using smart appliances management System-A case study

A substantial quantity of energy is consumed by residential buildings. The appliance runs on a set schedule in most of the houses. As a result, the appliance remains switched ON whether user need it or not. This results in significant energy waste and high electricity bill. To address this problem a smart energy management system (SEMS) has been proposed that generates switch ON/OFF control actions for appliances depending on physical characteristics. The SEMS optimize the energy waste and energy cost by reducing the operative time of the appliances without affecting the user comfort. The SEMS further reduces the electricity bill, if public utility offer the time varying electricity price for the residential consumers. A residential building microgrid system with smart appliances, rooftop mounted photovoltaic system, energy storage (electric car, and battery) linked to the public utility is regarded to assess the performance of the SEMS. The performance of the SEMS is tested for both flat and real time electricity price system. The potentiality and functioning of SEMS are verified by measuring energy and environmental performance, as well as user comfort and acceptance of the control system, throughout the course of a normal day. The findings show a potential for energy savings of 31% and a electricity energy cost reduction by 35%. The appliance smart scheduling and management of appliances according to the renewable power generation or the grid electricity cost are reflecting the novality and originality of the proposed SEMS system.

Simulation and power quality analysis of a Loose-Coupled bipolar DC microgrid in an office building

With distributed generation and battery storage technologies thriving in microgrids, the use of direct current (DC) microgrids in the building sector offers multiple advantages in energy efficiency and power quality compared with alternating current (AC) systems. This study developed a new concept of a loose-coupled bipolar DC building power system. The concept was used to design a real-world office building in Shenzhen, China. A power system model was developed to study the stability and control of the DC power system and to verify DC power quality. The design and modeling of the DC power control system is discussed in detail. The study developed a few common fault scenarios in DC building microgrids that were simulated in the MATLAB-Simulink environment to validate the design of a loose-coupled bipolar DC system. The results indicate that the proposed loose-coupled bipolar DC system schema, when implemented with proper control algorithms, can achieve good fault-tolerant performance with reliable power quality, even during disruptive system events.

Robust Optimization of Building Microgrid Considering the Uncertainty of the Aggregated Power of Thermostatically Controlled Loads

Robust Optimization of Building Microgrid Considering the Uncertainty of the Aggregated Power of Thermostatically Controlled Loads

A techno-economic assessment on the adoption of latent heat thermal energy storage systems for district cooling optimal dispatch & operations

The adoption of high-density phase change materials as cold thermal energy storage media have been considered as potential alternatives to water and ice. In this paper, the implementation of these media has been investigated in the context of district cooling in mixed-used building micro-grids whereby the cold thermal energy storage is retrofitted into an existing eco-building cluster. The techno-economic feasibility of implementing phase change materials, which includes eutectic salt, polyethylene glycol and paraffin as cold thermal energy storage media for district cooling, have been evaluated by computing the economic dispatch of a cooling bus network using mixed-integer quadratic programming. Furthermore, the impact of high-density storage media with a reduced footprint on the integration and design of cold thermal energy storage systems in district cooling has been included in the analysis. The capital expenditure of cold thermal energy storage systems, the land rental price for required occupancy and operating expenditure are used to determine the net present value after 20 years. Through peak shaving, optimally sized cold thermal energy storage has shown a better alternative substituting one of the two chillers. The smaller chiller (1200 kWc) is no longer required in the plant, giving significant savings in capital expenditure. The cost-benefit analysis also showed that only eutectic salt phase change materials are competitive with ice, with a net present value approaching 200,000 $ after 20 years. When phase change materials are adopted, especially if compared with water, the building operations and related costs are significantly less affected by large fluctuations in land rental price given its smaller footprint, thus posing significantly lower financial risks.

Research on the Stackelberg Game Method of Building Micro-grid with Electric Vehicles

A “micro-grid to users” Stackelberg game method with electric vehicles (EVs) is constructed with the aim of addressing the problem of unstable generation of renewable energy. Micro-grids set charging electricity prices and EV discharging electricity prices based on the supply and demand of electrical energy to achieve maximum benefits. The user as a follower formulates the electricity consumption and discharge strategy according to the electricity price to achieve the highest electricity satisfaction and the lowest cost. This proves theoretically that only one Stackelberg equilibrium exists in this game. The feasibility of the method is verified through numerical simulation and the advantages of the proposed method are analysed.