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Connection of electric storage technologies to smartgrids will have substantial implications for building energy systems. Local storage will enable demand response. When connected to buildings, mobile storage devices such as electric vehicles (EVs) are in competition with conventional stationary sources at the building. EVs can change the financial as well as environmental attractiveness of on-site generation (e.g. PV or fuel cells). In order to examine the impact of EVs on building energy costs and 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 and solved for 2020 technology assumptions. The mixedinteger linear program is applied to a set of 139 different commercial buildings in California and example results as well as the aggregated economic and environmental benefits are reported. Special constraints for the available PV, solar thermal, and EV parking lots at the commercial buildings are considered. The research shows that EV batteries can be used to reduce utility-related energy costs at the smart grid or commercial building due to arbitrage of energy between buildings with different tariffs. However, putting more emphasis on CO2 emissions makes stationary storage more attractive and stationary storage capacities increase while the attractiveness of EVs decreases. The limited availability of EVs at the commercial building decreases the attractiveness of EVs and if PV is chosen by the optimization, then it is mostly used to charge the stationarystorage at the commercial building and not the EVs connected to the building.

Connecting electric storage technologies to smartgrids will have substantial implications in building energy systems. Local storage will enable demand response. Mobile storage devices in electric vehicles (EVs) are in direct competition with conventional stationary sources at the building. EVs will change the financial as well as environmental attractiveness of on-site generation (e.g. PV, or fuel cells). In order to examine the impact of EVs on building energy costs and CO2 emissions in 2020, 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 buildings in California and example results as well as the aggregated economic and environmental benefits are reported. The research shows that considering second life of EV batteries might be very beneficial for commercial buildings.

In an effort to create broad access to its optimization software, Lawrence Berkeley National Laboratory (LBNL), in collaboration with the University of California at Davis (UC Davis) and OSISoft, has recently developed a Software as a Service (SaaS) Model for reducing energy costs, cutting peak power demand, and reducing carbon emissions for multipurpose buildings. UC Davis currently collects and stores energy usage data from buildings on its campus. Researchers at LBNL sought to demonstrate that a SaaS application architecture could be built on top of this data system to optimize the scheduling of electricity and heat delivery in the building. The SaaS interface, known as WebOpt, consists of two major parts: a) the investment & planning and b) the operations module, which builds on the investment & planning module. The operational scheduling and load shifting optimization models within the operations module use data from load prediction and electrical grid emissions models to create an optimal operating schedule for the next week, reducing peak electricity consumption while maintaining quality of energy services. LBNL's application also provides facility managers with suggested energy infrastructure investments for achieving their energy cost and emission goals based on historical data collected with OSISoft's system. This paper describes these models as well as the SaaS architecture employed by LBNL researchers to provide asset scheduling services to UC Davis. The peak demand, emissions, and cost implications of the asset operation schedule and investments suggested by this optimization model are analyzed.

In an effort to create broad access to its optimization software, Lawrence Berkeley National Laboratory (LBNL), in collaboration with the University of California at Davis (UC Davis) and OSISoft, has recently developed a Software as a Service (SaaS) Model for reducing energy costs, cutting peak power demand, and reducing carbon emissions for multipurpose buildings. UC Davis currently collects and stores energy usage data from buildings on its campus. Researchers at LBNL sought to demonstrate that a SaaS application architecture could be built on top of this data system to optimize the scheduling of electricity and heat delivery in the building. The SaaS interface, known as WebOpt, consists of two major parts: a) the investment & planning and b) the operations module, which builds on the investment & planning module. The operational scheduling and load shifting optimization models within the operations module use data from load prediction and electrical grid emissions models to create an optimal operating schedule for the next week, reducing peak electricity consumption while maintaining quality of energy services. LBNL's application also provides facility managers with suggested energy infrastructure investments for achieving their energy cost and emission goals based on historical data collected with OSISoft's system. This paper describes these models as well as the SaaS architecture employed by LBNL researchers to provide asset scheduling services to UC Davis. The peak demand, emissions, and cost implications of the asset operation schedule and investments suggested by this optimization model are analysed.

Connection of electric storage technologies to smartgrids or microgrids will have substantial implications for building energy systems. In addition to potentially supplying ancillary services directly to the traditional centralized grid (or macrogrid), local storage will enable demand response. As an economically attractive option, mobile storage devices such as plug-in electric vehicles (EVs) are in direct competition with conventional stationary sources and storage at the building. In general, it is assumed that they can improve the financial as well as environmental attractiveness of renewable and fossil based on-site generation (e.g. PV, fuel cells, or microturbines operating with or without combined heat and power). Also, mobile storage can directly contribute to tariff driven demand response in commercial buildings. In order to examine the impact of mobile storage on building energy costs and carbon dioxide (CO2) emissions, a microgrid/distributed-energy-resources (DER) adoption problem is formulated as a mixed-integer linear program with minimization of annual building energy costs applying CO2 taxes/CO2 pricing schemes. The problem is solved for a representative office building in the San Francisco Bay Area in 2020. By using employees' EVs for energy management, the office building can arbitrage its costs. But since the car battery lifetime is reduced, a business model that also reimburses car owners for the degradation will be required. In general, the link between a microgrid and an electric vehicle can create a win-win situation, wherein the microgrid can reduce utility costs by load shifting while the electric vehicle owner receives revenue that partially offsets his/her expensive mobile storage investment. For the California office building with EVs connected under a business model that distributes benefits, it is found that the economic impact is very limited relative to the costs of mobile storage for the site analyzed, i.e. cost reductions from electric vehicle connections are modest. Nonetheless, this example shows that some economic benefit is created because of avoided demand charges and on-peak energy. The strategy adopted by the office building is to avoid these high on-peak costs by using energy from the mobile storage in the business hours. CO2 emission reduction strategy results indicate that EVs' contribution at the selected office building are minor.

Non-residential sectors offer many promising applications for electrical storage (batteries) and photovoltaics (PVs). However, choosing and operating storage under complex tariff structures poses a daunting technical and economic problem that may discourage potential customers and result in lost carbon and economic savings. Equipment vendors are unlikely to provide adequate environmental analysis or unbiased economic results to potential clients, and are even less likely to completely describe the robustness of choices in the face of changing fuel prices and tariffs. Given these considerations, researchers at Lawrence Berkeley National Laboratory (LBNL) have designed the Storage Viability and Optimization Web Service (SVOW): a tool that helps building owners, operators and managers to decide if storage technologies and PVs merit deeper analysis. SVOW is an open access, web-based energy storage and PV analysis calculator, accessible by secure remote login. Upon first login, the user sees an overview of the parameters: load profile, tariff, technologies, and solar radiation location. Each parameter has a pull-down list of possible predefined inputs and users may upload their own as necessary. Since the non-residential sectors encompass a broad range of facilities with fundamentally different characteristics, the tool starts by asking the users to select a load profile from a limited cohort group of example facilities. The example facilities are categorized according to their North American Industry Classification System (NAICS) code. After the load profile selection, users select a predefined tariff or use the widget to create their own. The technologies and solar radiation menus operate in a similar fashion. After these four parameters have been inputted, the users have to select an optimization setting as well as an optimization objective. The analytic engine of SVOW is LBNL?s Distributed Energy Resources Customer Adoption Model (DER-CAM), which is a mixed-integer linear program (MILP) written and executed in the General Algebraic Modeling System (GAMS) optimization software. LBNL has released version 1.2.0.11 of SVOW. Information can be found at http://der.lbl.gov/microgrids-lbnl/current-project-storage-viability-website.

The Ernest Orlando Lawrence Berkeley National Laboratory (LBNL) is working with the California Energy Commission (CEC) to determine the potential role of commercial-sector distributed energy resources (DER) with combined heat and power (CHP) in greenhouse gas emissions (GHG) reductions. Historically, relatively little attention has been paid to the potential of medium-sized commercial buildings with peak electric loads ranging from 100 kW to 5 MW. In our research, we examine how these medium-sized commercial buildings might implement DER and CHP. The buildings are able to adopt and operate various technologies, e.g., photovoltaics (PV), on-site thermal generation, heat exchangers, solar thermal collectors, absorption chillers, batteries and thermal storage systems. We apply the Distributed Energy Resources Customer Adoption Model (DER-CAM), which is a mixed-integer linear program (MILP) that minimizes a site?s annual energy costs and/or CO2 emissions. Using 138 representative mid-sized commercial sites in California, existing tariffs of major utilities, and expected performance data of available technologies in 2020, we find the GHG reduction potential for these buildings. We compare different policy instruments, e.g., a CO2 pricing scheme or a feed-in tariff (FiT), and show their contributions to the California Air Resources Board (CARB) goals of additional 4 GW CHP capacities and 6.7 Mt/a GHG reduction in California by 2020. By applying different price levels for CO2, we find that there is competition between fuel cells and PV/solar thermal. It is found that the PV/solar thermal adoption increases rapidly, but shows a saturation at high CO2 prices, partly due to limited space for PV and solar thermal. Additionally, we find that large office buildings are good hosts for CHP in general. However, most interesting is the fact that fossil-based CHP adoption also increases with increasing CO2 prices. We will show service territory specific results since the attractiveness of DER varies widely by climate zone and service territory.

The Ernest Orlando Lawrence Berkeley National Laboratory (LBNL) is working with the California Energy Commission (CEC) to determine the potential role of commercial sector distributed generation (DG) with combined heat and power (CHP) capability deployment in greenhouse gas emissions (GHG) reductions. CHP applications at large industrial sites are well known, and a large share of their potential has already been harvested. In contrast, relatively little attention has been paid to the potential of medium-sized commercial buildings, i.e. ones with peak electric loads ranging from 100 kW to 5 MW. We examine how this sector might implement DG with CHP in cost minimizing microgrids that are able to adopt and operate various energy technologies, such as solar photovoltaics (PV), on-site thermal generation, heat exchangers, solar thermal collectors, absorption chillers, and storage systems. We apply a mixed-integer linear program (MILP) that minimizes a site?s annual energy costs as its objective. Using 138 representative mid-sized commercial sites in California (CA), existing tariffs of three major electricity distribution ultilities, and performance data of available technology in 2020, we find the GHG reduction potential for this CA commercial sector segment, which represents about 35percent of total statewide commercial sector sales. Under the assumptions made, in a reference case, this segment is estimated to be capable of economically installing 1.4 GW of CHP, 35percent of the California Air Resources Board (CARB) statewide 4 GW goal for total incremental CHP deployment by 2020. However, because CARB?s assumed utilization is far higher than is found by the MILP, the adopted CHP only contributes 19percent of the CO2 target. Several sensitivity runs were completed. One applies a simple feed-in tariff similar to net metering, and another includes a generous self-generation incentive program (SGIP) subsidy for fuel cells. The feed-in tariff proves ineffective at stimulating CHP deployment, while the SGIP buy down is more powerful. The attractiveness of CHP varies widely by climate zone and service territory, but in general, hotter inland areas and San Diego are the more attractive regions because high cooling loads achieve higher equipment utilization. Additionally, large office buildings are surprisingly good hosts for CHP, so large office buildings in San Diego and hotter urban centers emerge as promising target hosts. Overall the effect on CO2 emissions is limited, never exceeding 27 percent of the CARB target. Nonetheless, results suggest that the CO2 emissions abatement potential of CHP in mid-sized CA buildings is significant, and much more promising than is typically assumed.

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, which allows us to estimate the needed technologies and costs for achieving a ZC building or microgrid.

The Ernest Orlando Lawrence Berkeley National Laboratory (LBNL) is working with the California Energy Commission (CEC) to determine the potential role of commercial sector distributed generation (DG) with combined heat and power (CHP) capability deployment in greenhouse gas emissions (GHG) reductions. CHP applications at large industrial sites are well known, and a large share of their potential has already been harvested. In contrast, relatively little attention has been paid to the potential of medium-sized commercial buildings, i.e., ones with peak electric loads ranging from 100 kW to 5 MW. We examine how this sector might implement DG with CHP in cost minimizing microgrids that are able to adopt and operate various energy technologies, such as solar photovoltaics (PV), on-site thermal generation, heat exchangers, solar thermal collectors, absorption chillers, and storage systems. We apply a mixed-integer linear program (MILP) that minimizes a site's annual energy costs as its objective. Using 138 representative mid-sized commercial sites in California (CA), existing tariffs of three major electricity distribution ultilities plus a natural gas company, and performance data of available technology in 2020, we find the GHG reduction potential for this CA commercial sector segment, which represents about 35percent of total statewide commercial sector sales. Under the assumptions made, in a reference case, this segment is estimated to be capable of economically installing 1.4 GW of CHP, 35percent of the California Air Resources Board (CARB) statewide 4 GW goal for total incremental CHP deployment by 2020. However, because CARB's assumed utilization is far higher than is found by the MILP, the adopted CHP only contributes 19percent of the CO2 target. Several sensitivity runs were completed. One applies a simple feed-in tariff similar to net metering, and another includes a generous self-generation incentive program (SGIP) subsidy for fuel cells. The feed-in tariff proves ineffective at stimulating CHP deployment, while the SGIP buy down is more powerful. The attractiveness of CHP varies widely by climate zone and service territory, but in general, hotter inland areas and San Diego are the more attractive regions because high cooling loads achieve higher equipment utilization. Additionally, large office buildings are surprisingly good hosts for CHP, so large office buildings in San Diego and hotter urban centers emerge as promising target hosts. Overall the effect on CO2 emissions is limited, never exceeding 27percent of the CARB target. Nonetheless, results suggest that the CO2 emissions abatement potential of CHP in mid-sized CA buildings is significant, and much more promising than is typically assumed.

Lawrence Berkeley National Laboratory (LBL) is working with the California Energy Commission (CEC) to determine the role of distributed generation (DG) in greenhouse gas reductions. The impact of DG on large industrial sites is well known, and mostly, the potentials are already harvested. In contrast, little is known about the impact of DG on commercial buildings with peak electric loads ranging from 100 kW to 5 MW. We examine how DG with combined heat and power (CHP) may be implemented within the context of a cost minimizing microgrid that is able to adopt and operate various smart energy technologies, such as thermal and photovoltaic (PV) on-site generation, heat exchangers, solar thermal collectors, absorption chillers, and storage systems. We use a mixed-integer linear program (MILP) that has the minimization of a site's annual energy costs as objective. Using 138 representative commercial sites in California (CA) with existing tariff rates and technology data, we find the greenhouse gas reduction potential for California's commercial sector. This paper shows results from the ongoing research project and finished work from a two year U.S. Department of Energy research project. To show the impact of the different technologies on CO2 emissions, several sensitivity runs for different climate zones within CA with different technology performance expectations for 2020 were performed. The considered sites can contribute between 1 Mt/a and 1.8 Mt/a to the California Air Resources Board (CARB) goal of 6.7Mt/a CO2 abatement potential in 2020. Also, with lower PV and storage costs as well as consideration of a CO2 pricing scheme, our results indicate that PV and electric storage adoption can compete rather than supplement each other when the tariff structure and costs of electricity supply have been taken into consideration. To satisfy the site's objective of minimizing energy costs, the batteries will be charged also by CHP systems during off-peak and mid-peak hours and not only by PV during sunny on-peak hours.

The US Department of Energy has launched the Zero-Net-Energy (ZNE) Commercial Building Initiative (CBI) in order to develop commercial buildings 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-efficient technologies and meet their remaining energy needs through on-site renewable energy generation. We examine how such buildings may be implemented within the context of a cost- or carbon-minimizing microgrid that is able to adopt and operate various technologies, such as photovoltaic (PV) on-site generation, heat exchangers, solar thermal collectors, absorption chillers, and passive / demand-response technologies. We use a mixed-integer linear program (MILP) that has a multi-criteria objective function: the minimization of a weighted average of the building's annual energy costs and carbon / 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 CBI. Using a nursing home in northern California and New York with existing tariff rates and technology data, we find that a ZNE building requires ample PV capacity installed to ensure electricity sales during the day. This is complemented by investment in energy-efficient combined heat and power 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 ZNE. For comparison, we analyze a nursing home facility in New York to examine the effects of a flatter tariff structure and different load profiles. It has trouble reaching ZNE status and its load reductions as well as efficiency measures need to be more effective than those in the CA case. Finally, we illustrate that the multi-criteria frontier that considers costs and carbon emissions in the presence of demand response dominates the one without it.

The increasing concern about climate change as well as the expected direct environmental economic impacts of global warming will put considerable constraints on the US building sector, which consumes roughly 48percent of the total primary energy, making it the biggest single source of CO2 emissions. It is obvious that the battle against climate change can only be won by considering innovative building approaches and consumer behaviors and bringing new, effective low carbon technologies to the building / consumer market. However, the limited time given to mitigate climate change is unforgiving to misled research and / or policy. This is the reason why Lawrence Berkeley National Lab is working on an open source long range Stochastic Lite Building Module (SLBM) to estimate the impact of different policies and consumer behavior on the market penetration of low carbon building technologies. SLBM is designed to be a fast running, user-friendly model that analysts can readily run and modify in its entirety through a visual interface. The tool is fundamentally an engineering-economic model with technology adoption decisions based on cost and energy performance characteristics of competing technologies. It also incorporates consumer preferences and passive building systems as well as interactions between technologies (such as internal heat gains). Furthermore, everything is based on service demand, e.g. a certain temperature or luminous intensity, instead of energy intensities. The core objectives of this paper are to demonstrate the practical approach used, to start a discussion process between relevant stakeholders and to build collaborations.

The addition of storage technologies such as flow batteries, conventional batteries, and heat storage can improve the economic, as well as environmental attraction of micro-generation systems (e.g., PV or fuel cells with or without CHP) and contribute to enhanced demand response. The interactions among PV, solar thermal, and storage systems can be complex, depending on the tariff structure, load profile, etc. In order to examine the impact of storage technologies on demand response and CO2 emissions, a microgrid's distributed energy resources (DER) adoption problem is formulated as a mixed-integer linear program that can pursue two strategies as its objective function. These two strategies are minimization of its annual energy costs or of its CO2 emissions. The problem is solved for a given test year at representative customer sites, e.g., nursing homes, to obtain not only the optimal investment portfolio, but also the optimal hourly operating schedules for the selected technologies. This paper focuses on analysis of storage technologies in micro-generation optimization on a building level, with example applications in New York State and California. It shows results from a two-year research project performed for the U.S. Department of Energy and ongoing work. Contrary to established expectations, our results indicate that PV and electric storage adoption compete rather than supplement each other considering the tariff structure and costs of electricity supply. The work shows that high electricity tariffs during on-peak hours are a significant driver for the adoption of electric storage technologies. To satisfy the site's objective of minimizing energy costs, the batteries have to be charged by grid power during off-peak hours instead of PV during on-peak hours. In contrast, we also show a CO2 minimization strategy where the common assumption that batteries can be charged by PV can be fulfilled at extraordinarily high energy costs for the site.

The addition of storage technologies such as flow batteries, conventional batteries, and heat storage can improve the economic as well as environmental attractiveness of on-site generation (e.g., PV, fuel cells, reciprocating engines or microturbines operating with or without CHP) and contribute to enhanced demand response. In order to examine the impact of storage technologies on demand response and carbon emissions, a microgrid's distributed energy resources (DER) adoption problem is formulated as a mixed-integer linear program that has the minimization of annual energy costs as its objective function. By implementing this approach in the General Algebraic Modeling System (GAMS), the problem is solved for a given test year at representative customer sites, such as schools and nursing homes, to obtain not only the level of technology investment, but also the optimal hourly operating schedules. This paper focuses on analysis of storage technologies in DER optimization on a building level, with example applications for commercial buildings. Preliminary analysis indicates that storage technologies respond effectively to time-varying electricity prices, i.e., by charging batteries during periods of low electricity prices and discharging them during peak hours. The results also indicate that storage technologies significantly alter the residual load profile, which can contribute to lower carbon emissions depending on the test site, its load profile, and its adopted DER technologies.

The deployment of small (

On-site cogeneration of heat and electricity, thermal and electrical storage, and curtailing/rescheduling demand options are often cost-effective to commercial and industrial sites. This collection of equipment and responsive consumption can be viewed as an integrated energy system(IES). The IES can best meet the sites cost or environmental objectives when controlled in a coordinated manner. However, continuously determining this optimal IES dispatch is beyond the expectations for operators of smaller systems. A new algorithm is proposed in this paper to approximately solve the real-time dispatch optimization problem for a generic IES containing an on-site cogeneration system subject to random outages, limited curtailment opportunities, an intermittent renewable electricity source, and thermal storage. An example demonstrates how this algorithm can be used in simulation to estimate the value of IES components.

Building energy simulation software (e.g., EnergyPlus) is a powerful tool used widely by designers and researchers. However, current software is limited in modeling distributed generation (DG), including DG with heat recovery applied to building end-use, i.e., combined heat and power (CHP). Concurrently, DG investment and dispatch optimization software has been developed, yet has not been linked to a building energy simulation program for accurate assessment of DG designs, particularly under uncertainty in future end-use loads and equipment availability. CHP is a proven approach to cost effective reductions in primary fuel consumption and CO2 emissions. Integrating DG system design and controls into building energy simulation is an important step towards popular DG acceptance. We propose to extend the existing building energy simulation program, EnergyPlus (E+), to enable the simulation of various DG modules and associated control strategies in order to achieve more accurate and holistic analysis of DG technologies. Extension of EnergyPlus is conveniently facilitated by SPARK, a program capable of modeling building equipment and controls as individual modules. These modules can be automatically integrated with EnergyPlus building models. Candidate DG systems can be selected from the DG investment optimization program, Distributed Energy Resources Customer Adoption Model (DER-CAM). The dispatch of the modeled DG system can be determined by a novel dispatch optimization algorithm, the Energy Manager, that accounts for uncertainty in future load and DG availability, as well as curtailment options. DG equipment and controls can modeled in SPARK and integrated into EnergyPlus building models. The way to this holistic approach will be described in this paper.

The European Union has established a number of targets regarding energy efficiency, Renewable Energy Sources (RES) and CO2 reductions as the 'GREEN PAPER on Energy Efficiency', the Directive for 'promotion of the use of bio-fuels or other renewable fuels for transport' or 'Directive of the European Parliament of the Council on the promotion of cogeneration based on a useful heat demand in the internal energy market'. A lot of the according RES and RUE measures are not attractive for investors from an economic point of view. Therefore governments all over the world have to spend public money to promote these technologies/measures to bring them into market. These expenditures have to be adjusted to budget concerns and should be spent most efficiently. Therefore, the spent money has to be dedicated to technologies and efficiency measures with the best yield in CO2 reduction without wasting money. The core question: "How can public money - for promoting sustainable energy systems - be spent most efficiently to reduce GHG-emissions?" has been well investigated by the European project Invert. In course of this project a simulation tool has been designed to answer this core question. This paper describes the modelling with the Invert simulation tool and shows the key features necessary for simulating the energy system. A definition of 'Promotion Scheme Efficiency' is given which allows estimating the most cost effective technologies and/or efficiency measures to reduce CO2 emissions. Investigations performed with the Invert simulation tool deliver an optimum portfolio mix of technologies and efficiency measures for each selected region. Within Invert seven European regions were simulated and for the Austrian case study the detailed portfolio mix is shown and political conclusions are derived.

This paper describes the economically optimal adoption and operation of distributed energy resources (DER) by a hypothetical California microgrid consisting of a group of commercial buildings over an historic test year, 1999. The optimisation is conducted using a customer adoption model (DER-CAM) developed at Berkeley Lab and implemented in the General Algebraic Modeling System (GAMS). A microgrid is a semiautonomous grouping of electricity and heat loads interconnected to the existing utility grid (macrogrid) but able to island from it. The microgrid minimises the cost of meeting its energy requirements (consisting of both electricity and heat loads) by optimising the installation and operation of DER technologies while purchasing residual energy from the local combined natural gas and electricity utility. The available DER technologies are small-scale generators (< 500 kW), such as reciprocating engines, microturbines, and fuel cells, with or without combined heat and power (CHP) equipment, such as water and space heating and/or absorption cooling. By introducing a tax on carbon emissions, it is shown that if the microgrid is allowed to install CHP-enabled DER technologies, its carbon emissions are mitigated more than without CHP, demonstrating the potential benefits of small-scale CHP technology for climate change mitigation. Reciprocating engines with heat recovery and/or absorption cooling tend to be attractive technologies for the mild southern California climate, but the carbon mitigation tends to be modest compared to purchasing utility electricity because of the predominance of relatively clean central station generation in California.

In this report, an economic model of customer adoption of distributed energy resources (DER) is developed. It covers progress on the DER project for the California Energy Commission (CEC) at Berkeley Lab during the period July 2001 through Dec 2002 in the Consortium for Electric Reliability Technology Solutions (CERTS) Distributed Energy Resources Integration (DERI) project. CERTS has developed a specific paradigm of distributed energy deployment, the CERTS Microgrid (as described in Lasseter et al. 2002). The primary goal of CERTS distributed generation research is to solve the technical problems required to make the CERTS Microgrid a viable technology, and Berkeley Lab's contribution is to direct the technical research proceeding at CERTS partner sites towards the most productive engineering problems. The work reported herein is somewhat more widely applicable, so it will be described within the context of a generic microgrid (mGrid). Current work focuses on the implementation of combined heat and power (CHP) capability. A mGrid as generically defined for this work is a semiautonomous grouping of generating sources and end-use electrical loads and heat sinks that share heat and power. Equipment is clustered and operated for the benefit of its owners. Although it can function independently of the traditional power system, or macrogrid, the mGrid is usually interconnected and exchanges energy and possibly ancillary services with the macrogrid. In contrast to the traditional centralized paradigm, the design, implementation, operation, and expansion of the mGrid is meant to optimize the overall energy system requirements of participating customers rather than the objectives and requirements of the macrogrid.

This paper describes the economically optimal adoption and operation of distributed energy resources (DER) by a hypothetical California microgrid ((mu)Grid) consisting of a group of commercial buildings over an historic test year, 1999. The optimisation is conducted using a customer adoption model (DER-CAM) developed at Berkeley Lab and implemented in the General Algebraic Modeling System (GAMS). A (mu)Grid is a semiautonomous grouping of electricity and heat loads interconnected to the existing utility grid (macrogrid) but able to island from it. The (mu)Grid minimises the cost of meeting its energy requirements (consisting of both electricity and heat loads) by optimising the installation and operation of DER technologies while purchasing residual energy from the local combined natural gas and electricity utility. The available DER technologies are small-scale generators (< 500 kW), such as reciprocating engines, microturbines, and fuel cells, with or without CHP equipment, such as water- and space-heating and/or absorption cooling. By introducing a tax on carbon emissions, it is shown that if the (mu)Grid is allowed to install CHP-enabled DER technologies, its carbon emissions are mitigated more than without CHP, demonstrating the potential benefits of small-scale CHP technology for climate change mitigation. Reciprocating engines with heat recovery and/or absorption cooling tend to be attractive technologies for the mild southern California climate, but the carbon mitigation tends to be modest compared to purchasing utility electricity because of the predominance of relatively clean generation in California.