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This paper introduces a linear battery aging and degradation model to a multi-energy microgrid sizing model using mixed integer linear programming. The battery aging model and its integration into a larger microgrid sizing formulation are described. A case study is provided to explore the impact of considering battery aging on key results: optimal photovoltaic and storage capacities, optimal distributed energy resources operations strategies, and annual cost and generation metrics. The case study results suggest that considering battery degradation in optimal microgrid sizing problems significantly impacts the perceived value of storage. Depending on capacity loss and lifetime targets, considering battery degradation is shown to decrease optimal storage capacities between 6 and 92% versus scenarios that do not consider battery health. When imposing constant distributed energy resource capacities, inclusion of degradation can decrease optimal annual battery cycling by as much as a factor five and reduce total annual electricity cost savings from otherwise identical photovoltaic and storage systems by 5–12%. These results emphasize that as batteries grow in maturity and ubiquity for distributed energy applications, considering battery health and capacity loss is an essential component of any analytical tool or model to guide system planning and decision-making.

The installation and operation distributed energy resources (DER) and the electrification of the heat supply significantly changes the interaction of the residential building stock with the grid infrastructure. Evaluating the mass deployment of DER at the national level would require analyzing millions of individual buildings, entailing significant computational burden. To overcome this, this work proposes a novel bottom-up model that consists of an aggregation algorithm to create a spatially distributed set of typical residential buildings from census data. Each typical building is then optimized with a Mixed-Integer Linear Program to derive its cost optimal technology adoption and operation, determining its changing grid load in future scenarios. The model is validated for Germany, with 200 typical buildings considered to sufficiently represent the diversity of the residential building stock. In a future scenario for 2050, photovoltaic and heat pumps are predicted to be the most economically and ecologically robust supply solutions for the different building types. Nevertheless, their electricity generation and demand temporally do not match, resulting in a doubling of the peak electricity grid load in the rural areas during the winter. The urban areas can compensate this with efficient co-generation units, which are not cost-efficient in the rural areas., Preprint submitted to Energy & Buildings

This paper proposes a new methodology to capture the impact of fast moving clouds on utility power demand charges observed in microgrids with photovoltaic (PV) arrays, generators, and electrochemical energy storage. It consists of a statistical approach to introduce sub-hourly events in the hourly economic accounting process. The methodology is implemented in the Distributed Energy Resources Customer Adoption Model (DER-CAM), a state of the art mixed integer linear model used to optimally size DER in decentralized energy systems. Results suggest that previous iterations of DER-CAM could undersize battery capacities. The improved model depicts more accurately the economic value of PV as well as the synergistic benefits of pairing PV with storage.

The large-scale deployment of distributed energy resources will produce reverse power flows, voltage, and congestion problems in the distribution networks. This paper proposes a novel optimization model to support distribution system operators planning future medium voltage distribution networks characterized by high penetration of behind-the-meter distributed energy resources. The optimization model defines the optimal mix, placement, and size of on-load tap charger transformers and energy storage devices with the objectives of mitigating network technical problems and minimizing both investment and operation costs. The proposed optimization model relaxes the non-convex formulation of the optimal power flow to a constrained second-order cone programming model and exactly linearizes the non-linear model of the on-load tap changer transformer via binary expansion scheme and big-M method. These two transformations reduce the computational burden of the optimization allowing it to be applicable to real-scale distribution grids, as demonstrated by the results. The numerical results also show that the joint optimization of energy storage devices and on-load tap changer transformers produces a more affordable and flexible planning strategy than the individual optimization of the technologies.

Author(s): Cardoso, Goncalo; Heleno, Miguel; DeForest, Nicholas | Abstract: The objective of the project is to develop an advanced optimization-based design support tool for AC or DC microgrids in remote locations, where utility grids may not be accessible. The mathematical model and the interface are being developed such that multiple design objectives and criteria/constraints can be easily enabled or disabled, to deliver a flexible tool relevant to a large, diverse user base, and to facilitate future feature developments. The new tool delivered by this project leverages the team’s extensive expertise in the development, testing, deployment, and commercialization of the state-of-the-art Distributed Energy Resources Customer Adoption Model (DER-CAM) – which is the foundation for the new tool. Phase I of the project leverages the microgrid design and decision support tool DER-CAM. Key Phase I developments include the following adaption to the DER-CAM model: a) AC or DC microgrid architectures, b) multiple economic objectives (e.g. single customer vs community vs utility perspective), c) constraints on fuel availability (e.g. total fuel available during outage periods), d) active and reactive power flow constraints for normal and N-1 contingency cases, e) component part load efficiencies (e.g. power electronic devices), and f) interactive data visualization capabilities (e.g. design topology elements). A component library including components’ default technical and economical characteristics was also developed. In Phase II, the project is focused on tool testing and validation, use for real-world microgrid designs, and outreach. Fully developing this tool will have the following impacts: 1) optimal off-grid microgrid designs that replace existing back of the envelope or non-optimal calculations, reducing capital costs and risk of microgrid deployment; 2) removing barriers to microgrid assessments by lowering microgrid soft costs, as the tool is freely usable; 3) reliable and resilient designs that reduce the cost of critical load shedding due to component outages.

In this study, we investigate the cost-effectiveness of hydrogen storage systems in industrial microgrids. This analysis is enabled by an extension introduced to the Distributed Energy Resources — Customer Adoption Model. We analyze the potential to mitigate costs associated with volumetric time-of-use rates through arbitrage, reduce peak loads, and optimize the use of on-site generation, amongst others. The analysis is supported by a case study using real load data from two manufacturing companies with significant heat loads. We find that hydrogen storage systems can be economically viable per se and can be competitive with other storage systems such as standard electrochemical storage. The use of a fuel cell with combined heat and power production further integrates sectors but does not lead to financial savings.

Thermal energy storage (TES) and distributed generation technologies, such as combined heat and power (CHP) or photovoltaics (PV), can be used to reduce energy costs and decrease CO2 emissions from buildings by shifting energy consumption to times with less emissions and/or lower energy prices. To determine the feasibility of investing in TES in combination with other distributed energy resources (DER), mixed integer linear programming (MILP) can be used. Such a MILP model is the well-established Distributed Energy Resources Customer Adoption Model (DER-CAM); however, it currently uses only a simplified TES model to guarantee linearity and short run-times. Loss calculations are based only on the energy contained in the storage. This paper presents a new DER-CAM TES model that allows improved tracking of losses based on ambient and storage temperatures, and compares results with the previous version. A multi-layer TES model is introduced that retains linearity and avoids creating an endogenous optimization problem. The improved model increases the accuracy of the estimated storage losses and enables use of heat pumps for low temperature storage charging. Results indicate that the previous model overestimates the attractiveness of TES investments for cases without possibility to invest in heat pumps and underestimates it for some locations when heat pumps are allowed. Despite a variation in optimal technology selection between the two models, the objective function value stays quite stable, illustrating the complexity of optimal DER sizing problems in buildings and microgrids.

Optimizing Distributed Energy Resources and I Building Retrofits with the Strategic DER- CAModel Michael Stadler 1,2, , Markus Groissbock 1,2 Goncalo Cardoso 1,3 , Chris Marnay 1 Ernest Orlando Lawrence Berkeley National Laboratory, One Cyclotron Road, MS: 90-1121, Berkeley, California 94720, USA Center for Energy and innovative Technologies (CET), Doberggasse 9, 3681 Hofamt Priel, Austria Instituto Superior Tecnico - Technical University of Lisbon, Portugal The Distributed Energy Resources Customer Adoption Model (DER-CAM) has been designed at Lawrence Berkeley National Laboratory (LBNL). DER-CAM has been funded partly by the Office of Electricity Delivery and Energy Reliability, Distributed Energy Program of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 . July 2014

Nowadays, a significant number of distribution systems are facing problems to accommodate more photovoltaic (PV) capacity, namely due to the overvoltages during the daylight periods. This has an impact on the private investments in distributed energy resources (DER), since it occurs exactly when the PV prices are becoming attractive, and the opportunity to an energy transition based on solar technologies is being wasted. In particular, this limitation of the networks is a barrier for larger consumers, such as commercial and public buildings, aiming at investing in PV capacity and start operating as microgrids connected to the MV network. To address this challenge, this paper presents a coordinated approach to the microgrid investment and planning problem, where the system operator and the microgrid owner collaborate to improve the voltage control capabilities of the distribution network, increasing the PV potential. The results prove that this collaboration has the benefit of increasing the value of the microgrid investments while improving the quality of service of the system and it should be considered in the future regulatory framework.

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.

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. Further investigation on the impacts of this approach on the prediction of fuel cell and micro-turbine performance is suggested.

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 with the stochastic formulation of the problem.

Even as distributed generation and distributed energy resources (DER) become a more appealing option to meeting building electricity, heat, and cooling demands, the process of selecting the appropriate equipment mix remains as a complex and time-consuming obstacle. This is the motivation behind Lawrence Berkeley National Laboratory's (LBNL) development of WebOpt, a flexible web-based software as service (SaaS) approach for optimizing the selection and operation of DER equipment, and running the Distributed Energy Resources Customer Adoption Model (DER-CAM), the optimization platform that supports it. DER-CAM solves energy systems for microgrids [1], [2] (e.g. combined heat and power (CHP) or electric storage) holistically, taking into account service levels for multiple building end-uses, including heating, cooling and electricity. Given an individual microgrid's hourly energy requirements, available technologies and the economic environment as defined by tariff structure, DER-CAM finds the economically or environmentally optimal combination of equipment to install and an optimal schedule to operate it [3], [4].

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.