Michael Stadler

Microgrids can integrate variable renewable energy sources into the energy system by controlling flexible assets locally. However, as the energy system is dynamic, an effective microgrid controller must be able to receive feedback from the system in real-time, plan ahead and take into account the active electricity tariff, to maximize the benefits to the operator. These requirements motivate the use of optimization-based control methods, such as Model Predictive Control to optimally dispatch… Show more

Microgrids can integrate variable renewable energy sources into the energy system by controlling flexible assets locally. However, as the energy system is dynamic, an effective microgrid controller must be able to receive feedback from the system in real-time, plan ahead and take into account the active electricity tariff, to maximize the benefits to the operator. These requirements motivate the use of optimization-based control methods, such as Model Predictive Control to optimally dispatch flexible assets in microgrids. However, the major bottleneck to achieve maximum benefits with these methods is their predictive accuracy. This paper addresses this bottleneck by developing a novel multi-step forecasting method for a Model Predictive Control framework. The presented methods are applied to a real test-bed of a renewable energy community in Austria, where its operational costs and CO2 emissions are benchmarked with those of a rule-based control strategy for Flat, Time-of-Use, Demand Charge and variable energy price tariffs. In addition, the impact of forecast errors and electric battery capacity on energy community operational savings are examined. The key results indicate that the proposed controller can outperform a rule-based dispatch strategy by 24.7% in operational costs and by 8.4% in CO2 emissions through optimal operation of flexibilities if it has perfect foresight. However, if the controller is deployed in a realistic environment, where forecasts for electrical load and PV generation are required, the same savings are reduced to 3.3% for cost and 7.3% for CO2, respectively. In such environments, the proposed controller performs best in highly dynamic tariffs such as Time-of-Use and Real-time pricing rates, achieving real cost savings of up to 6.3%. These results show that the profitability of optimization-based control of microgrids is threatened by forecast errors. Show less

Book chapter in "Handbook of Electrical Power Supply: Energy Technology and Industry in Dialogue" https://www.degruyter.com/document/doi/10.1515/978311075358 that describes how Microgrids/Cellular Energy Systems can be planned in a standardized and innovative way. Currently, only available in German.

Local and renewable energy communities show a high potential for the efficient use of distributed energy technologies at regional levels according to the Clean Energy Package of the European Union. However, until now there are only limited possibilities to bring such energy communities into reality because of several limitation factors. Challenges are already encountered during the planning phase since a large number of decision variables have to be considered depending on the number and type… Show more

Local and renewable energy communities show a high potential for the efficient use of distributed energy technologies at regional levels according to the Clean Energy Package of the European Union. However, until now there are only limited possibilities to bring such energy communities into reality because of several limitation factors. Challenges are already encountered during the planning phase since a large number of decision variables have to be considered depending on the number and type of community participants and distributed technologies. This paper overcomes these challenges by establishing a mixed-integer linear programming based optimal planning approach for renewable energy communities. A real case study is analyzed by creating an energy community testbed with a leading energy service provider in Austria. The case study considers nine energy community members of a municipality in Austria, distributed photovoltaic systems, energy storage systems, different electricity tariff scenarios and market signals including feed-in tariffs. The key results indicate that renewable energy communities can significantly reduce the total energy costs by 15% and total carbon dioxide emissions by 34% through an optimal selection and operation of the energy technologies. In all the optimization scenarios considered, each community participant can benefit both economically and ecologically. Show less

The intra-hour intermittency of solar energy and demand introduce significant design challenges for microgrids. To avoid costly energy shortfalls and mitigate outage probability, islanded microgrids must be designed with sufficient distributed energy resources (DER) to meet demand and fulfill the energy and power balance. To avoid excessive runtime, current design tools typically only utilize hourly data. As such, the variable nature of solar and demand is often overlooked. Thus, DER designed… Show more

The intra-hour intermittency of solar energy and demand introduce significant design challenges for microgrids. To avoid costly energy shortfalls and mitigate outage probability, islanded microgrids must be designed with sufficient distributed energy resources (DER) to meet demand and fulfill the energy and power balance. To avoid excessive runtime, current design tools typically only utilize hourly data. As such, the variable nature of solar and demand is often overlooked. Thus, DER designed based on hourly data may result in significant energy shortfalls when deployed in real-world conditions. This research introduces a new, fast method for optimizing DER investments and performing dispatch planning to consider intra-hour variability. A novel set of constraints which operate on intra-hour data are implemented in a mixed-integer-linear-program microgrid investment optimization. Variability is represented by the single worst-case intra-hour fluctuation. This allows for fast optimization times compared to other approaches tested. Applied to a residential microgrid case study with 5-minute intra-hour resolution, this new method is shown to maintain optimality within 2% and reduce runtime by 98.2% compared to full-scale-optimizations which consider every time-step explicitly. Applicable to a variety of technologies and demand types, this method provides a general framework for incorporating intra-hour variability into microgrid design. Show less

This paper investigates the optimal planning of microgrids including the hydrogen energy
system through mixed-integer linear programming model. A real case study is analyzed by
extending the only microgrid lab facility in Austria. The case study considers the hydrogen
production via electrolysis, seasonal storage and fueling station for meeting the hydrogen
fuel demand of fuel cell vehicles, busses and trucks. The optimization is performed relative
to two different reference… Show more

This paper investigates the optimal planning of microgrids including the hydrogen energy
system through mixed-integer linear programming model. A real case study is analyzed by
extending the only microgrid lab facility in Austria. The case study considers the hydrogen
production via electrolysis, seasonal storage and fueling station for meeting the hydrogen
fuel demand of fuel cell vehicles, busses and trucks. The optimization is performed relative
to two different reference cases which satisfy the mobility demand by diesel fuel and utility
electricity based hydrogen fuel production respectively. The key results indicate that the
low emission hydrogen mobility framework is achieved by high share of renewable energy
sources and seasonal hydrogen storage in the microgrid. The investment optimization
scenarios provide at least 66% and at most 99% carbon emission savings at increased costs
of 30% and 100% respectively relative to the costs of the diesel reference case (current
situation). Show less

The optimal design of microgrids with thermal energy system requires optimization techniques that can provide investment and scheduling of the technology portfolio involved. In the modeling of such systems with seasonal storage capability, the two main challenges include the low temporal resolution of available data and the non-linear cost versus capacity relationship of solar thermal and heat storage technologies. This work overcomes these challenges by developing two different optimization… Show more

The optimal design of microgrids with thermal energy system requires optimization techniques that can provide investment and scheduling of the technology portfolio involved. In the modeling of such systems with seasonal storage capability, the two main challenges include the low temporal resolution of available data and the non-linear cost versus capacity relationship of solar thermal and heat storage technologies. This work overcomes these challenges by developing two different optimization models based on mixed-integer linear programming with objectives to minimize the total energy costs and carbon dioxide emissions. Piecewise affine functions are used to approximate the non-linear cost versus capacity behavior. The developed methods are applied to the optimal planning of a case study in Austria. The results of the models are compared based on the accuracy and real-time performance together with the impact of piecewise affine cost functions versus non-piecewise affine fixed cost functions. The results show that the investment decisions of both models are in good agreement with each other while the computational time for the 8760-h based model is significantly greater than the model having three representative periods. The models with piecewise affine cost functions show larger capacities of technologies than non-piecewise affine fixed cost function based models. Show less

Recently, researchers have begun to study hybrid approaches to Microgrid techno-economic planning, where a reduced model optimizes the DER selection and sizing is combined with a full model that optimizes operation and dispatch. Though providing significant computation time savings, these hybrid models are susceptible to infeasibilities, when the size of the DER is insufficient to meet the energy balance in the full model during macrogrid outages. In this work, a novel hybrid optimization… Show more

Recently, researchers have begun to study hybrid approaches to Microgrid techno-economic planning, where a reduced model optimizes the DER selection and sizing is combined with a full model that optimizes operation and dispatch. Though providing significant computation time savings, these hybrid models are susceptible to infeasibilities, when the size of the DER is insufficient to meet the energy balance in the full model during macrogrid outages. In this work, a novel hybrid optimization framework is introduced, specifically designed for resilience to macrogrid outages. The framework solves the same optimization problem twice, where the second solution using full data is informed by the first solution using representative data to size and select DER. This framework includes a novel constraint on the state of charge for storage devices, which allows the representation of multiple repeated days of grid outage, despite a single 24-h profile being optimized in the representative model. Multiple approaches to the hybrid optimization are compared in terms of their computation time, optimality, and robustness against infeasibilities. Through a case study on three real Microgrid designs, we show that allowing optimizing the DER sizing in both stages of the hybrid design, dubbed minimum investment optimization (MIO), provides the greatest degree of optimality, guarantees robustness, and provides significant time savings over the benchmark optimization. Show less

Mixed Integer Linear Programming (MILP) optimization algorithms provide accurate and clear solutions for Microgrid and Distributed Energy Resources projects. Full-scale optimization approaches optimize all time-steps of data sets (e.g., 8760 time-step and higher resolutions), incurring extreme and unpredictable run-times, often prohibiting such approaches for effective Microgrid designs. To reduce run-times down-sampling approaches exist. Given that the literature evaluates the full-scale and… Show more

Mixed Integer Linear Programming (MILP) optimization algorithms provide accurate and clear solutions for Microgrid and Distributed Energy Resources projects. Full-scale optimization approaches optimize all time-steps of data sets (e.g., 8760 time-step and higher resolutions), incurring extreme and unpredictable run-times, often prohibiting such approaches for effective Microgrid designs. To reduce run-times down-sampling approaches exist. Given that the literature evaluates the full-scale and down-sampling approaches only for limited numbers of case studies, there is a lack of a more comprehensive study involving multiple Microgrids. This paper closes this gap by comparing results and run-times of a full-scale 8760 h time-series MILP to a peak preserving day-type MILP for 13 real Microgrid projects. The day-type approach reduces the computational time between 85% and almost 100% (from 2 h computational time to less than 1 min). At the same time the day-type approach keeps the objective function (OF) differences below 1.5% for 77% of the Microgrids. The other cases show OF differences between 6% and 13%, which can be reduced to 1.5% or less by applying a two-stage hybrid approach that designs the Microgrid based on down-sampled data and then performs a full-scale dispatch algorithm. This two stage approach results in 20%–99% run-time savings. Show less

With energy systems, the problem of economic planning is decisive in the design of a low carbon and resilient future grid. Although several tools to solve the problem already exist in literature and industry, most tools only consider a single “typical year” while providing investment decisions that last around a quarter of a century. In this paper, we introduce why such an approach is limited and derive two approaches to correct this. The first approach, the Forward-Looking model, assumes… Show more

With energy systems, the problem of economic planning is decisive in the design of a low carbon and resilient future grid. Although several tools to solve the problem already exist in literature and industry, most tools only consider a single “typical year” while providing investment decisions that last around a quarter of a century. In this paper, we introduce why such an approach is limited and derive two approaches to correct this. The first approach, the Forward-Looking model, assumes perfect knowledge and makes investment decisions based on the full planning horizon. The second novel approach, the Adaptive method, solves the optimization problem in single year iterations, making incremental investment decisions that are dependant on previous years, with only knowledge of the current year. Comparing the two approaches on a realistic microgrid, we find little difference in investment decisions (maximum 21% difference in total cost over 20 years), but large differences in optimization time (up to 12000% time difference). We close the paper by discussing implications of forecasting errors on the microgrid planning process, concluding that the Adaptive approach is a suitable choice. Show less

Computational time in optimization models scales with the number of time steps. To save time, solver time resolution can be reduced and input data can be down-sampled into representative periods such as one or a few representative days per month. However, such data reduction can come at the expense of solution accuracy. In this work, the impact of reduction of input data is systematically isolated considering an optimization which solves an energy system using representative days. A new data… Show more

Computational time in optimization models scales with the number of time steps. To save time, solver time resolution can be reduced and input data can be down-sampled into representative periods such as one or a few representative days per month. However, such data reduction can come at the expense of solution accuracy. In this work, the impact of reduction of input data is systematically isolated considering an optimization which solves an energy system using representative days. A new data reduction method aggregates annual hourly demand data into representative days which preserve demand peaks in the original profiles. The proposed data reduction approach is tested on a real energy system and real annual hourly demand data where the system is optimized to minimize total annual costs. Compared to the full-resolution optimization of the energy system, the total annual energy cost error is found to be equal or less than 0.22% when peaks in customer demand are preserved. Errors are significantly larger for reduction methods that do not preserve peak demand. Solar photovoltaic data reduction effects are also analyzed. This paper demonstrates a need for data reduction methods which consider demand peaks explicitly. Show less

Currently, many Microgrid projects remain financially uncertain and not bankable for institutional investors due to major challenges in existing planning and design methods that require multiple, complex steps and software tools. Existing techniques treat every Microgrid project as a unique system, resulting in expensive, non-standardized approaches and implementations which cannot be compared. That is, it is not possible to correlate the results from different planning methods performed by… Show more

Currently, many Microgrid projects remain financially uncertain and not bankable for institutional investors due to major challenges in existing planning and design methods that require multiple, complex steps and software tools. Existing techniques treat every Microgrid project as a unique system, resulting in expensive, non-standardized approaches and implementations which cannot be compared. That is, it is not possible to correlate the results from different planning methods performed by different project developers and/or engineering companies. This very expensive individual process cannot guarantee financial revenue streams, cannot be reliably audited, impedes pooling of multiple Microgrid projects into a financial asset class, nor does it allow for wide-spread and attractive Microgrid and Distributed Energy Resource projects deployment. Thus, a reliable, integrated, and streamlined process is needed that guides the Microgrid developer and engineer through conceptual design, engineering, detailed electrical design, implementation, and operation in a standardized and data driven approach, creating reliable results and financial indicators that can be audited and repeated by investors and financers. This article describes the steps and methods involved in creating bankable Microgrids by relying on an integrated Microgrid planning software approach that unifies proven technologies and tested planning methods, researched and developed by the United States National Laboratory System as well as the US Department of Energy, to reduce design times.
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In developing countries, mini-grids are seen as an important option to improve electrification rates in rural areas. In order to be successful, mini-grids face issues of operation and sizing of generation capacities. Current studies on the optimal sizing of mini-grids do not include capacity expansion feedbacks regarding the operator’s or investor’s long-term economic performance on growth in electricity usage, e.g. gap between demand and supply impacting the operator’s income. Using a System… Show more

In developing countries, mini-grids are seen as an important option to improve electrification rates in rural areas. In order to be successful, mini-grids face issues of operation and sizing of generation capacities. Current studies on the optimal sizing of mini-grids do not include capacity expansion feedbacks regarding the operator’s or investor’s long-term economic performance on growth in electricity usage, e.g. gap between demand and supply impacting the operator’s income. Using a System Dynamics model, this paper compares the impact from two capacity expansion strategies on rural mini-grid operator’s long-term economic performance. The two capacity expansion strategies are: a strategy with minimized costs and a strategy where only diesel power is allowed. Research shows that a cost-minimized capacity expansion strategy might not be the most beneficial solution for the operator’s long term financial performance. Specifically, the high investment costs prohibit the implementation of the cost-minimized expansion strategy. In addition, the diesel-only expansion strategy suffers from high operational costs, which creates long-term challenges as the share of diesel increase. Therefore, the timeline of the investments and when to implement different strategies is important, creating a benefit for a System Dynamics approach. Show less

Keynote speech at Trends, Facts, and Myths of the Energy System of the Future, Fact Check Energy Transition 2017/2018

Energy supply in rural and off-grid communities has traditionally relied on diesel-based microgrids, due to limited access. But global environmental concerns are pushing for the transformation of these systems into renewable-based microgrids. This transition to more complex systems with a mix of dispatchable and non-dispatchable resources requires new planning tools that ensure the security of supply. This paper presents a novel mixed-integer linear optimization model that determines optimal… Show more

Energy supply in rural and off-grid communities has traditionally relied on diesel-based microgrids, due to limited access. But global environmental concerns are pushing for the transformation of these systems into renewable-based microgrids. This transition to more complex systems with a mix of dispatchable and non-dispatchable resources requires new planning tools that ensure the security of supply. This paper presents a novel mixed-integer linear optimization model that determines optimal technology mix, size, placement, and associated dispatch for a multi-energy microgrid. The model satisfies microgrid’s electrical and heat transfer network limitations by integrating linear power flow and heat transfer equations. It captures the efficiency gains from waste heat recovery through combined heat and power technologies, by modeling the interplay between electrical and heat sources. To ensure a secure design against generator outages, the optimization maintains sufficient reserve capacity in the system, which is dynamically allocated based on system operating conditions. Several case studies on an isolated microgrid model, developed based on a real microgrid in Alaska, illustrate how the proposed model works. The results show the effectiveness of the model and are used to discuss various aspects of the optimization solution. Show less

Scientists answer 33 questions about our future. Michael Stadler's contribution "Are we running out of energy?"

Microgrid resource sizing problems typically include the analysis of a combination of value streams such as peak shaving, load shifting, or load scheduling, which support the economic feasibility of the microgrid deployment. However, microgrid benefits can go beyond these, and the ability to provide ancillary grid services such as frequency regulation or spinning and non-spinning reserves is well known, despite typically not being considered in resource sizing problems. This paper proposes the… Show more

Microgrid resource sizing problems typically include the analysis of a combination of value streams such as peak shaving, load shifting, or load scheduling, which support the economic feasibility of the microgrid deployment. However, microgrid benefits can go beyond these, and the ability to provide ancillary grid services such as frequency regulation or spinning and non-spinning reserves is well known, despite typically not being considered in resource sizing problems. This paper proposes the expansion of the Distributed Energy Resources Customer Adoption Model (DER-CAM), a state-of-the-art microgrid resource sizing model, to include revenue streams resulting from the participation in ancillary service markets. Results suggest that participation in such markets may not only influence the optimum resource sizing, but also the operational dispatch, with results being strongly influenced by the exact market requirements and clearing prices. Show less

Radio Talk-Show on electricity systems, 10:00 to 11:00 Central European Time Zone, 11 April 2017.

Austrian newspaper article with a renewable energy section from Michael Stadler

Optimal microgrid design is a challenging problem, especially for multi-energy microgrids with electricity, heating, and cooling loads as well as sources, and multiple energy carriers. To address this problem, this paper presents an optimization model formulated as a mixed-integer linear program, which determines the optimal technology portfolio, the optimal technology placement, and the associated optimal dispatch, in a microgrid with multiple energy types. The developed model uses a… Show more

Optimal microgrid design is a challenging problem, especially for multi-energy microgrids with electricity, heating, and cooling loads as well as sources, and multiple energy carriers. To address this problem, this paper presents an optimization model formulated as a mixed-integer linear program, which determines the optimal technology portfolio, the optimal technology placement, and the associated optimal dispatch, in a microgrid with multiple energy types. The developed model uses a multi-node modeling approach (as opposed to an aggregate single-node approach) that includes electrical power flow and heat flow equations, and hence, offers the ability to perform optimal siting considering physical and operational constraints of electrical and heating/cooling networks. The new model is founded on the existing optimization model DER-CAM, a state-of-the-art decision support tool for microgrid planning and design. The results of a case study that compares single-node vs. multi-node optimal design for an example microgrid show the importance of multi-node modeling. It has been shown that single-node approaches are not only incapable of optimal DER placement, but may also result in sub-optimal DER portfolio, as well as underestimation of investment costs. Show less

STADLER Michael: Smartgrids and Microgrids, TV Talk-Show Science.talk, 7. December 2016 TV Channel ORF 3, Austria and ARD-alpha, Germany, only available in German.

Climate change and Paris climate change agreement

Microgrids are an increasingly common component of the evolving electricity grids with the potential to improve local reliability, reduce costs, and increase penetration rates for distributed renewable generation. The additional complexity of microgrids often leads to increased investment costs, creating a barrier for widespread adoption. These costs may result directly from specific needs for islanding detection, protection systems and power quality assurance that would otherwise be avoided in… Show more

Microgrids are an increasingly common component of the evolving electricity grids with the potential to improve local reliability, reduce costs, and increase penetration rates for distributed renewable generation. The additional complexity of microgrids often leads to increased investment costs, creating a barrier for widespread adoption. These costs may result directly from specific needs for islanding detection, protection systems and power quality assurance that would otherwise be avoided in simpler system configurations. However, microgrids also facilitate additional value streams that may make up for their increased costs and improve the economic viability of microgrid deployment. This paper analyses the literature currently available on research relevant to value streams occurring in microgrids that may contribute to offset the increased investment costs. A review on research related to specific microgrid requirements is also presented. Show less

In the course of the European Project Energy Efficiency and Risk Management in public buildings (EnRiMa), a mathematical model has been needed, predicting the room air temperatures based on the physical properties of the thermal zone and weather forecasts. Existing models based on physical building properties and weather forecasts did not deliver acceptable results. Based on the hypothesis that the missing thermal mass in the existing models is the main reason for the unacceptable results, a… Show more

In the course of the European Project Energy Efficiency and Risk Management in public buildings (EnRiMa), a mathematical model has been needed, predicting the room air temperatures based on the physical properties of the thermal zone and weather forecasts. Existing models based on physical building properties and weather forecasts did not deliver acceptable results. Based on the hypothesis that the missing thermal mass in the existing models is the main reason for the unacceptable results, a model based on physical properties and weather forecast, including the storage mass of a building has been developed. Based on this developed model and real data from a test site, Campus Pinkafeld of the University of Applied Science Burgenland, Austria, the model has been verified and validated. With the new developed model it is possible to predict the occurring room air temperature for a whole day with a maximum deviation of approximately ±1 K, which increases the precision compared to other models. Show less

Distributed energy resources gain an increased importance in commercial and industrial building design. Combined heat and power (CHP) units are considered as one of the key technologies for cost and emission reduction in buildings. In order to make optimal decisions on investment and operation for these technologies, detailed system models are needed. These models are often formulated as linear programming problems to keep computational costs and complexity in a reasonable range. However, CHP… Show more

Distributed energy resources gain an increased importance in commercial and industrial building design. Combined heat and power (CHP) units are considered as one of the key technologies for cost and emission reduction in buildings. In order to make optimal decisions on investment and operation for these technologies, detailed system models are needed. These models are often formulated as linear programming problems to keep computational costs and complexity in a reasonable range. However, CHP systems involve variations of the efficiency for large nameplate capacity ranges and in case of part load operation, which can be even of non-linear nature. Since considering these characteristics would turn the models into non-linear problems, in most cases only constant efficiencies are assumed. This paper proposes possible solutions to address this issue. For a mixed integer linear programming problem two approaches are formulated using binary and Special-Ordered-Set (SOS) variables. Both suggestions have been implemented into the optimization model DER–CAM to simulate investment decisions of CHP micro-turbines and CHP fuel cells with variable efficiencies. The approaches have further been applied successfully in a case study with four different commercial buildings. Comparison of the results between the standard version and the new approaches indicate that total annual system costs remain almost unchanged. System performance is subject to change and storage technologies become more important. Part load operation has mainly been found important for fuel cell units. The micro-turbine is found almost exclusively in full load, thus rendering the application of the new approaches for this technology unnecessary for the considered unit sizes and building types. The approach using binary variables was the most promising method to model variable efficiencies in terms of computational costs and results. It should especially be considered for specific fuel cell technologies... Show less

The pressuring need to reduce the import of fossil fuels as well as the need to dramatically reduce CO2 emissions in Europe motivated the European Commission (EC) to implement several regulations directed to building owners. Most of these regulations focus on increasing the number of energy efficient buildings, both new and retrofitted, since retrofits play an important role in energy efficiency. Overall, this initiative results from the realization that buildings will have a significant impact… Show more

The pressuring need to reduce the import of fossil fuels as well as the need to dramatically reduce CO2 emissions in Europe motivated the European Commission (EC) to implement several regulations directed to building owners. Most of these regulations focus on increasing the number of energy efficient buildings, both new and retrofitted, since retrofits play an important role in energy efficiency. Overall, this initiative results from the realization that buildings will have a significant impact in fulfilling the 20/20/20-goals of reducing the greenhouse gas emissions by 20%, increasing energy efficiency by 20%, and increasing the share of renewables to 20%, all by 2020.

The Distributed Energy Resources Customer Adoption Model (DER-CAM) is an optimization tool used to support DER investment decisions, typically by minimizing total annual costs or CO2 emissions while providing energy services to a given building or microgrid site. This paper shows enhancements made to DER-CAM to consider building retrofit measures along with DER investment options. Specifically, building shell improvement options have been added to DER-CAM as alternative or complementary options to investments in other DER such as PV, solar thermal, combined heat and power, or energy storage. The extension of the mathematical formulation required by the new features introduced in DER-CAM is presented and the resulting model is demonstrated at an Austrian Campus building by comparing DER-CAM results with and without building shell improvement options. Strategic investment results are presented and compared to the observed investment decision at the test site. Results obtained considering building shell improvement options suggest an optimal weighted average U value of about 0.53 W/(m2 K) for the test site. This result is approximately 25% higher than what is currently observed in the building, suggesting that the retrofits made in 2002 were not optimal... Show less

This paper presents an investigation of the economic benefit of thermal energy storage (TES) for cooling, across a range of economic and climate conditions. Chilled water TES systems are simulated for a large office building in four distinct locations, Miami in the U.S.; Lisbon, Portugal; Shanghai, China; and Mumbai, India. Optimal system size and operating schedules are determined using the optimization model DER-CAM, such that total cost, including electricity and amortized capital costs are… Show more

This paper presents an investigation of the economic benefit of thermal energy storage (TES) for cooling, across a range of economic and climate conditions. Chilled water TES systems are simulated for a large office building in four distinct locations, Miami in the U.S.; Lisbon, Portugal; Shanghai, China; and Mumbai, India. Optimal system size and operating schedules are determined using the optimization model DER-CAM, such that total cost, including electricity and amortized capital costs are minimized. The economic impacts of each optimized TES system is then compared to systems sized using a simple heuristic method, which bases system size as fraction (50% and 100%) of total daily on-peak summer cooling loads. Results indicate that TES systems of all sizes can be effective in reducing annual electricity costs (5–15%) and peak electricity consumption (13–33%). The investigation also identifies a number of criteria which drive TES investment, including low capital costs, electricity tariffs with high power demand charges and prolonged cooling seasons. In locations where these drivers clearly exist, the heuristically sized systems capture much of the value of optimally sized systems; between 60% and 100% in terms of net present value. However, in instances where these drivers are less pronounced, the heuristic tends to oversize systems, and optimization becomes crucial to ensure economically beneficial deployment of TES, increasing the net present value of heuristically sized systems by as much as 10 times in some instances. Show less

Deregulation of the energy sector has created new markets for producers as well as oppor-
tunities for consumers to meet their needs in a more customized way. Yet, traditional building
energy management systems operate statically by adjusting air or water flow in heating and
cooling systems in response to pre-determined triggers, viz., large deviations in the zone tem-
perature from the equipment’s set-point temperature. Here, we provide decision support to
operators of buildings… Show more

Deregulation of the energy sector has created new markets for producers as well as oppor-
tunities for consumers to meet their needs in a more customized way. Yet, traditional building
energy management systems operate statically by adjusting air or water flow in heating and
cooling systems in response to pre-determined triggers, viz., large deviations in the zone tem-
perature from the equipment’s set-point temperature. Here, we provide decision support to
operators of buildings via dynamic management of the installed equipment that seeks to mini-
mize energy costs. Assuming that the building’s occupants have comfort preferences expressed
by upper and lower limits for the temperature, we model the effect of active equipment control
(via changes to either the set point or the valve flow) on the zone temperature taking into ac-
count the external temperature, solar gains, the building’s shell, and internal loads. The energy
required to change the zone temperature in each time period is then used to calculate the en-
ergy cost in the objective function of an optimization problem. By implementing our model for
actual public buildings, we demonstrate the advantages of more active equipment management
in terms of lower costs and energy consumption.
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The large scale penetration of electric vehicles (EVs) will introduce technical challenges to the distribution grid, but also carries the potential for vehicle-to-grid services. Namely, if available in large enough numbers, EVs can be used as a distributed energy resource (DER) and their presence can influence optimal DER investment and scheduling decisions in microgrids. In this work, a novel EV fleet aggregator model is introduced in a stochastic formulation of DER-CAM [1], an optimization… Show more

The large scale penetration of electric vehicles (EVs) will introduce technical challenges to the distribution grid, but also carries the potential for vehicle-to-grid services. Namely, if available in large enough numbers, EVs can be used as a distributed energy resource (DER) and their presence can influence optimal DER investment and scheduling decisions in microgrids. In this work, a novel EV fleet aggregator model is introduced in a stochastic formulation of DER-CAM [1], an optimization tool used to address DER investment and scheduling problems. This is used to assess the impact of EV interconnections on optimal DER solutions considering uncertainty in EV driving schedules. Optimization results indicate that EVs can have a significant impact on DER investments, particularly if considering short payback periods. Furthermore, results suggest that uncertainty in driving schedules carries little significance to total energy costs, which is corroborated by results obtained using the stochastic formulation of the problem. Show less

Most recent improvements in battery and electric vehicle (EV) technologies, combined with some favorable off-peak charging rates and an enormous PV potential, make California a prime market for electric vehicle as well as stationary storage adoption. However, EVs or plug-in hybrids, which can be seen as a mobile energy storage, connected to different buildings throughout the day, constitute distributed energy resources (DER) markets and can compete with stationary storage, onsite energy… Show more

Most recent improvements in battery and electric vehicle (EV) technologies, combined with some favorable off-peak charging rates and an enormous PV potential, make California a prime market for electric vehicle as well as stationary storage adoption. However, EVs or plug-in hybrids, which can be seen as a mobile energy storage, connected to different buildings throughout the day, constitute distributed energy resources (DER) markets and can compete with stationary storage, onsite energy production (e.g. fuel cells, PV) at different building sites. Sometimes mobile storage is seen linked to renewable energy generation (e.g. PV) or as resource for the wider macro-grid by providing ancillary services for grid-stabilization. In contrast, this work takes a fundamentally different approach and considers buildings as the main hub for EVs / plug-in hybrids and considers them as additional resources for a building energy management system (EMS) to enable demand response or any other building strategy (e.g. carbon dioxide reduction). To examine the effect of, especially, electric storage technologies on building energy costs and carbon dioxide (CO2) emissions, a distributed-energy resources adoption problem is formulated as a mixed-integer linear program with minimization of annual building energy costs or CO2 emissions. The mixed-integer linear program is applied to a set of 139 different commercial building types in California, and the aggregated economic and environmental benefits are reported. To show the robustness of the results, different scenarios for battery performance parameters are analyzed. The results show that the number of EVs connected to the California commercial buildings depend mostly on the optimization strategy (cost versus CO2) of the building EMS and not on the battery performance parameters. The complexity of the DER interactions at buildings also show ... Show less

The U.S. Department of Energy has launched the commercial building initiative (CBI) in pursuit of its research goal of achieving zero-net-energy commercial buildings (ZNEB), i.e. ones that produce as much energy as they use. Its objective is to make these buildings marketable by 2025 such that they minimize their energy use through cutting-edge, energy-efficiency technologies and meet their remaining energy needs through on-site renewable energy generation. This paper examines how such… Show more

The U.S. Department of Energy has launched the commercial building initiative (CBI) in pursuit of its research goal of achieving zero-net-energy commercial buildings (ZNEB), i.e. ones that produce as much energy as they use. Its objective is to make these buildings marketable by 2025 such that they minimize their energy use through cutting-edge, energy-efficiency technologies and meet their remaining energy needs through on-site renewable energy generation. This paper examines how such buildings may be implemented within the context of a cost- or CO2-minimizing microgrid that is able to adopt and operate various technologies: photovoltaic modules (PV) and other on-site generation, heat exchangers, solar thermal collectors, absorption chillers, and passive/demand-response technologies. A mixed-integer linear program (MILP) that has a multi-criteria objective function is used. The objective is minimization of a weighted average of the building’s annual energy costs and CO2 emissions. The MILP’s constraints ensure energy balance and capacity limits. In addition, constraining the building’s energy consumed to equal its energy exports enables us to explore how energy sales and demand-response measures may enable compliance with the ZNEB objective. Using a commercial test site in northern California with existing tariff rates and technology data, we find that a ZNEB requires ample PV capacity installed to ensure electricity sales during the day. This is complemented by investment in energy-efficient combined heat and power (CHP) equipment, while occasional demand response shaves energy consumption. A large amount of storage is also adopted, which may be impractical. Nevertheless, it shows the nature of the solutions and costs necessary to achieve a ZNEB. Additionally, the ZNEB approach does not necessary lead to zero-carbon (ZC) buildings as is frequently argued. We also show a multi-objective frontier for the CA example, ...
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