Building Microgrid Research Articles

A Multi-Agent System-Based Approach for Optimal Operation of Building Microgrids with Rooftop Greenhouse

In this paper, an optimal energy management scheme for building microgrids with rooftop greenhouse is proposed. A building energy management system (BEMS) is utilized for the optimal fulfilment of energy demands in the building and the greenhouse. The exhaust heat generated due to the operation of air conditioners in the building is used for fulfilling the cooling demands of the greenhouse via chillers. In addition to thermal and cooling demands, the four major control parameters (temperature, humidity, light intensity, and CO2 concentration) are also considered for optimal growth of crops in the greenhouse. A multi-agent system (MAS) is adopted to realize the interaction among several households of the building, the greenhouse, and the BEMS. The MAS comprises of several inner-level, intermediate level, and upper-level agents, which are responsible for their respective tasks. The performance of the proposed optimization strategy is evaluated for two seasons of a year, i.e., summer and winter. Numerical simulations have demonstrated the effectiveness of the proposed operation scheme for optimal operation of building microgrids with rooftop greenhouses.

A two stage hierarchical control approach for the optimal energy management in commercial building microgrids based on local wind power and PEVs

The inclusion of plug-in electrical vehicles (PEVs) in microgrids not only could bring benefits by reducing the on-peak demand, but could also improve the economic efficiency and increase the environmental sustainability. Therefore, in this paper a two stage energy management strategy for the contribution of PEVs in demand response (DR) programs of commercial building microgrids is addressed. The main contribution of this work is the incorporation of the uncertainty of electricity prices in a model predictive control (MPC) based plan for energy management optimization. First, the optimization problem considers the operation of PEVs and wind power in order to optimize the energy management in the commercial building. Second, the total charged power reference which is computed for PEVs in this stage is sent to the PEVs control section so that it could be allocated to each PEV. Therefore, the power balance can be achieved between the power supply and the load in the proposed microgrid building while the operational cost is minimized. The predicted values for load demand, wind power, and electricity price are forecasted by a seasonal autoregressive integrated moving average (SARIMA) model. In addition, the conditional value at risk (CVaR) is used for the uncertainty in the electricity prices. In the end, the results confirm that the PEVs can effectively contribute in the DR programs for the proposed microgrid model.

Optimal operation of building microgrids – comparison with mixed-integer linear and continuous non-linear programming approaches

PurposeThis paper aims to present two mathematical models to solve the Energy Management problem of a building microgrid (MG). In particular, it proposes a deterministic mixed integer linear programming (MILP) and non-linear programming (NLP) formulations. This paper focuses on the modelling process and the optimization performances for both approaches regarding optimal operation of near-zero energy buildings connected to an electric MG with a 24-h time horizon.Design/methodology/approachA general architecture of a MG is detailed, involving energy storage systems, distributed generation and a thermal reduced model of the grid-connected building. A continuous non-linear model is detailed along with linearizations for the mixed-integer liner formulation. Multi-physic, non-linear and non-convex phenomena are detailed, such as ventilation and air quality models.FindingsResults show that both approaches are relevant for solving the energy management problem of the building MG.Originality/valueIntroduction and modelling of the thermal loads within the MG. The resulting linear program handles the mutli-objective trade-off between discomfort and the cost of use taking into account air quality criterion. Linearization and modelling of the ventilation system behaviour, which is generally non-linear and non-convex equality constraints, involving air quality model, heat transfer and ventilation power. Comparison of both MILP and NLP methods on a general use case provides a solution that can be interpreted for implementation.

CVaR-based energy management scheme for optimal resilience and operational cost in commercial building microgrids

This paper aims at enhancing the resilience of a photovoltaic-based microgrid equipped with battery storage, supplying a typical commercial building. When extreme weather conditions such as hurricane, tsunami and similar events occur, leading to islanding of the microgrid from the main power grid, it is not expected that the microgrid would be taken out of service for a long time. At the same time, it is not cost effective to make the electrical system to be absolutely reliable to provide service for the customers. The main contribution of this paper lies in its ability to determine the optimal point between the operational cost and grid resilience. In other words, this work proposes an optimal management system of battery energy storage in a way to enhance the resilience of the proposed microgrid while maintaining its operational cost at a minimum level. The optimization is achieved by solving a linear optimization programming problem while the Conditional Value at Risk (CVaR) is incorporated in the objective function. The CVaR is used to account for the uncertainty in the intermittent PV system generated power and that in the electricity price. Simulation analyses are carried out in MATLAB to evaluate the performance of the proposed method. Results reveal that the commercial building microgrid resilience is improved remarkably at a slight increase in the operational cost.

Optimal Energy Management of Combined Cooling, Heat and Power in Different Demand Type Buildings Considering Seasonal Demand Variations

In this paper, an optimal energy management strategy for a cooperative multi-microgrid system with combined cooling, heat and power (CCHP) is proposed and has been verified for a test case of building microgrids (BMGs). Three different demand types of buildings are considered and the BMGs are assumed to be equipped with their own combined heat and power (CHP) generators. In addition, the BMGs are also connected to an external energy network (EEN), which contains a large CHP, an adsorption chiller (ADC), a thermal storage tank, and an electric heat pump (EHP). By trading the excess electricity and heat energy with the utility grid and EEN, each BMG can fulfill its energy demands. Seasonal energy demand variations have been evaluated by selecting a representative day for the two extreme seasons (summer and winter) of the year, among the real profiles of year-round data on electricity, heating, and cooling usage of all the three selected buildings. Especially, the thermal energy management aspect is emphasized where, bi-lateral heat trading between the energy supplier and the consumers, so-called energy prosumer concept, has been realized. An optimization model based on mixed integer linear programming has been developed for minimizing the daily operation cost of the EEN while fulfilling the energy demands of the BMGs. Simulation results have demonstrated the effectiveness of the proposed strategy.

A Control Strategy of DC Building Microgrid Connected to the Neighborhood and AC Power Network

Recently, the use of DC microgrid distribution system has become more attractive than traditional AC systems due to their energy efficiency and ability to easily integrate with renewable energy sources and batteries. This paper proposes a 500 V DC microgrid which consists of a 20 kWp photovoltaic panel, batteries, and DC loads. A hierarchical control strategy to ensure balance power of the DC microgrid and the maintenance of common DC bus voltage is presented. The capability of exchanging power energy of the microgrid with the power system of neighborhood buildings is also considered. Typical operation modes are simulated in the Matlab/simulink environment to confirm the good performance of the controllers and the efficiency of appropriately controlling the charge–discharge of the battery system. This research is expected to bring benefits to the design and operation of the system, such as reducing the capacity of batteries, increasing the self-supply of buildings, and decreasing the electricity demand from the AC grid.

Model Predictive Control Approach for Building Microgrid Considering Dynamic Thermal Characteristics of Building

Abstract A simple dynamic model to simulate heating/cooling energy consumption in a building was proposed in this paper. The model consists of several transient energy balance equations for external walls and internal air, in which the convective heat transfer, conductive heat transfer and heat storage in the heat transfer process are considered. The proposed model has been implemented utilizing the SIMULINK/MATLAB platform. Then, a model predictive control (MPC) approach for the building Microgrid considering the dynamic thermal characteristics of the building was proposed. The MPC approach aims to minimize the total energy consumption of the building Microgrid by combining the model predictive and the short-term control, and guarantees the customer temperature comfort level at the same time. Two comparative cases are presented to verify the effectiveness of the proposed MPC approach.

Modular energy cost optimization for buildings with integrated microgrid

Buildings are becoming suitable for application of sophisticated energy management approaches to increase their energy efficiency and possibly turn them into active energy market participants. The paper proposes a modular coordination mechanism between building zones comfort control and building microgrid energy flows control based on model predictive control. The approach opens possibilities to modularly coordinate technologically heterogeneous building subsystems for economically-optimal operation under user comfort constraints. The imposed modularity is based on a simple interface for exchanging building consumption and microgrid energy price profiles. This is a key element for technology separation, replication and up-scaling towards the levels of smart grids and smart cities where buildings play active roles in energy management. The proposed coordination mechanism is presented in a comprehensive realistic case study of maintaining comfort in an office building with integrated microgrid. The approach stands out with significant performance improvements compared to various non-coordinated predictive control schemes and baseline controllers. Results give detailed information about yearly cost-effectiveness of the considered configurations, which are suitable for deployment as short- and long- term zero-energy building investments.

Hierarchical Management for Building Microgrid Considering Virtual Storage System and Plug-in Electric Vehicles

This paper aims to develop a hierarchical management strategy for the building microgrid. The proposed hierarchical framework is presented as day-ahead scheduling stage and a two-layer intra-hour adjustment stage. Firstly, a virtual storage system (VSS) model is developed by utilizing the heat storage characteristics of a building. Then, the VSS model is integrated into the developed hierarchical management strategy of the building microgrid, which is presented as a day-ahead optimal economic scheduling stage for the minimum operation cost and an intra-hour adjustment stage to follow the tie-line power set-points. In the intra-hour real-time adjustment, a master–client structure is designed. The VSS and plug-in electric vehicles (EVs) are coordinated by a method with two different time scales in order to execute the schedule and handle uncertainties from the load demand, outdoor environment and the renewable generation in the intra-hour adjustment stage. Numerical studies demonstrate that the proposed strategy can contribute to the operation cost reduction in day-ahead scheduling stage and tie-line power smoothing in intra-hour adjustment stage, while guarantee the customer temperature comfort level and EVs travelling demands at the same time.

A simple modular multilevel inverter topology for the power quality improvement in renewable energy based green building microgrids

The advent of multilevel inverter topologies paves path for the realization of renewable energy based microgrids for various classes of consumers. But, these topologies are offering concerns with design complexity, power quality, dv/dt stresses, capacitor voltage balancing, etc. In view of these challenges, this paper proposes a simple Modular Cascaded Multilevel Inverter with modified Uni-Polar Shifted Carrier Pulse Width Modulation (MCMI-mUPSC PWM) topology for microgrids. The proposed MCMI topology eliminates the use of clamping diodes, capacitors, and requires only 1/4th (25%) of the DC input voltage that is used in any conventional topology to produce the same amount of output voltage. This leads to the reduction in switching devices’ rating and dv/dt stresses. The proposed mUPSC PWM reduces the magnitude of hazardous lower order harmonics by shifting them to higher order components around the region of integral multiples of switching frequency. Thus, the proposed MCMI-mUPSC PWM topology simplifies the design of multilevel inverters along with aforesaid advantages. For the analysis, microgrid modelling and simulations are carried out in MATLAB/Simulink®. The effectiveness of the proposed topology is evaluated by calculating various power quality indices under dynamic and non-linear loading conditions. These indices are compared with the conventional topology with respect to standard tolerances. From results, it is observed that the proposed topology has improved the power quality along with the elimination of some of the inherent problems of the multilevel inverters. This enhances the plausibility of microgrid installations towards smart and net-zero energy buildings.

Control and Communication Protocols Based on Packetized Direct Load Control in Smart Building Microgrids

Recent communication, computation, and technology advances coupled with climate change concerns have transformed the near future prospects of electricity transmission, and, more notably, distribution systems and microgrids. Distributed resources (wind and solar generation, combined heat and power) and flexible loads (storage, computing, EV, HVAC) make it imperative to increase investment and improve operational efficiency. Commercial and residential buildings, being the largest energy consumption group among flexible loads in microgrids, have the largest potential and flexibility to provide demand-side management. Recent advances in networked systems and the anticipated breakthroughs of the Internet of Things will enable significant advances in demand-response capabilities of intelligent load networks of power-consuming devices such as HVAC components, water heaters, EV charging stations, and many more. In this paper, a new operating framework, called packetized direct load control (PDLC), is proposed based on the notion of quantization of energy demand. This control protocol is built on top of two communication protocols that carry either complete or binary information regarding the operation status of appliances. We discuss the optimal demand-side operation for both protocols and analytically derive the performance differences between the protocols. We propose an optimal reservation strategy for traditional and renewable energy for the PDLC in both day-ahead and real-time markets. At the end of the paper, we discuss the fundamental tradeoff between achieving controllability and endowing consumer choice and flexibility.

An Energy Scheduling Algorithm Supporting Power Quality Management in Commercial Building Microgrids

This paper presents an energy scheduling algorithm for a small-scale microgrid serving small to medium size commercial buildings (the building microgrid) that includes conventional and renewable distributed generation resources, energy storage, and both linear and nonlinear loads. An essential study objective is to mitigate power quality issues through coordinating the operating schedules of sensitive devices in the building microgrid. The proposed energy scheduling algorithm is formulated as a mixed integer programming problem where power quality requirements are modeled in the constraints. The algorithm also involves validation with the harmonics and dynamic event simulations. Case studies have been performed with realistic model parameters to verify the performance of the algorithm. This paper results demonstrate the effectiveness of the algorithm in managing voltage and frequency deviations, as well as harmonic distortions. In the transaction-based control framework, the proposed algorithm can be used to aggregate device transaction bids and facilitate the buildings-to-grid integration.

Energy management for a commercial building microgrid with stationary and mobile battery storage

This paper investigates the Demand Side Management (DSM) in a commercial building microgrid with solar generation, stationary Battery Energy Management System (BESS) and gridable (V2G) Electric Vehicle (EV) integration. Taking into consideration of a comprehensive pricing model, we first formulate a deterministic DSM as a mixed integer linear programming problem, assuming perfect knowledge of the uncertainties in the system. A two-stage stochastic DSM is further developed that addresses the stochastic nature in solar generation, loads, EV availabilities and EV energy demands. The proposed DSMs are validated with real solar generation, loads, BESS and EV data using sample average approximation. Detailed case studies show that the stochastic DSM outperforms its deterministic counterpart for cost saving for a wide range of prices, though at the expense of higher computational time. Computational results also demonstrate that moderate number of EVs helps to cut down the overall operation cost, which sheds light on the benefit of future large scale EV integration to smart buildings.

A Heuristic Operation Strategy for Commercial Building Microgrids Containing EVs and PV System

Commercial building microgrids will play an important role in the smart energy city. Stochastic and uncoordinated electric vehicle (EV) charging activities, which may cause performance degradations and overloads, have put great stress on the distribution system. In order to improve the self-consumption of PV energy and reduce the impact on the power grid, a heuristic operation strategy for commercial building microgrids is proposed. The strategy is composed of three parts: the model of EV feasible charging region, the mechanism of dynamical event triggering, and the algorithm of real-time power allocation for EVs. Furthermore, in order to lower the cost of computation resource, the strategy is designed to operate without forecasting on photovoltaic output or EV charging demand. A comprehensive result obtained from simulation tests has shown that the proposed strategy has both satisfactory results and high efficiency, which can be utilized in embedded systems for real-time allocation of EV charging rate.

Voltage Control Scheme with Distributed Generation and Grid Connected Converter in a DC Microgrid

Direct Current (DC) microgrids are expected to become larger due to the rapid growth of DC energy sources and power loads. As the scale of the system expends, the importance of voltage control will be increased to operate power systems stably. Many studies have been performed on voltage control methods in a DC microgrid, but most of them focused only on a small scale microgrid, such as a building microgrid. Therefore, a new control method is needed for a middle or large scale DC microgrid. This paper analyzes voltage drop problems in a large DC microgrid and proposes a cooperative voltage control scheme with a distributed generator (DG) and a grid connected converter (GCC). For the voltage control with DGs, their location and capacity should be considered for economic operation in the systems. Accordingly, an optimal DG allocation algorithm is proposed to minimize the capacity of a DG for voltage control in DC microgrids. The proposed methods are verified with typical load types by a simulation using MATLAB and PSCAD/EMTDC.

Electric Vehicle Charging in an Office Building Microgrid With Distributed Energy Resources

This paper discusses the charging of plug-in hybrid electric vehicles (PHEVs) in an existing office building microgrid equipped with a photovoltaic (PV) system and a combined heat and power (CHP) unit. Different charging strategies and charging power ratings for workplace charging are examined for their grid impact and their impact on the self-consumption of the locally generated electricity. The grid impact can be significantly reduced by using strategies that require limited future knowledge of the EV mobility behavior and limited communication infrastructure. These strategies allow a high number of EVs to be charged at an office building, even with a limited number of charging spots, due to the large standstill times.

Grid-price-dependent energy management in microgrids using a modified simulated annealing triple-optimizer

This article introduces a modified simulated annealing triple-optimizer for finding the optimal energy management strategy in terms of financial gain maximization in grid-connected, storage-augmented, photovoltaics-supplied prosumer building microgrids in a variable grid price scenario. For evaluating the performance of the optimizer, a number of test cases are specified offering different trading options to the prosumers. Obtained results are compared to a total state space search reference method and demonstrate that the simulated annealing approach was for all test cases able to find a globally optimal or close to optimal solution in significantly less computation time than the total space search reference method.

Microgrids of Commercial Buildings: Strategies to Manage Mode Transfer From Grid Connected to Islanded Mode

Microgrid systems located within commercial premises are becoming increasingly popular and their dynamic behavior is still uncharted territory in modern power networks. Improved understanding in design and operation is required for the electricity utility and building services design sectors. This paper evaluates the design requirements for a commercial building microgrid system to facilitate seamless mode transition considering an actual commercial building microgrid system. A dynamic simulation model of the proposed microgrid system is established (utilizing DIgSILENT Power Factory) to aid the development of planning and operational philosophy for the practical system. An economic operational criterion is developed for the microgrid to incorporate selective mode transition in different time intervals and demand scenarios. In addition, a multi-droop control strategy has been developed to mitigate voltage and frequency variations during mode transition. Different system conditions considering variability in load and generation are analyzed to examine the responses of associated microgrid network parameters (i.e., voltage and frequency) with the proposed mode transition strategy during planned and unplanned islanding conditions. It has been demonstrated that despite having a rigorous mode transition strategy, control of certain loads such as direct online (DOL) and variable-speed-drive (VSD) driven motor loads is vital for ensuring seamless mode-transition, in particular for unplanned islanding conditions.

Development of a thermal and electrical energy management in residential building micro-grid

Global warming and pressing concern about CO2 emission along with increasing fuel and oil cost have brought about great challenges for energy companies and homeowners. In this regard, a potential candidate solution is widely used for Distributed Energy Resources, which are capable of providing high quality, low-cost heat and power to off-grid or remote facilities. To appropriately manage thermal and electrical energy, a Smart Energy Management System (SEMS) with hierarchical control scheme has been presented. The developed SEMS model results in mixed integer non-linear programming optimization problem with the objective function of minimizing the operation cost as well as considering emissions. Moreover, the optimization problem has been solved for deterministic and stochastic scheduling algorithms. The novelty of this work is basically reliant on using data mining approach to reduce forecasting error. Several case studies have been carried out to evaluate the performance of proposed data mining method on both energy cost and expected cost.

Applications of optimal building energy system selection and operation

Berkeley Lab has been developing the Distributed Energy Resources Customer Adoption Model for several years. Given load curves for energy services requirements in a building microgrid (µ·grid), fuel costs and other economic inputs, and a menu of available technologies, the model finds the optimum equipment fleet and operating schedule. This capability is being applied using a Software as a Service (SaaS) model. The evolution of this approach is demonstrated by description of four past and present projects: (1) a public access web site focused on solar photovoltaic generation and battery viability for large non-residential customers; (2) a building CO2 emissions reduction operations problem for a university dining hall with potential investments considered; (3) a battery and rolling operating schedule problem for a large county jail; and (4) the direct control of the solar-assisted heating ventilation and air conditioning system of a university building by providing optimised daily schedules that are automatically implemented in the building’s energy management and control system. Together these examples show that optimisation of building μ·grid design and operation can be effectively achieved using SaaS.