Distributed Energy Resources Customer Adoption Research Articles

Community microgrid planning in Lombok Island: an Indonesian case study

Rural electrification, diesel generator replacement, and resilient electrification systems against natural disasters are among the main targets for Perusahaan Listrik Negara (PLN) in Indonesia to achieve a universally accessible, resilient, and environment-friendly electricity supply. Microgrids, therefore, become a popular and available way to achieve the aforementioned targets due to their flexibility and resiliency. This paper aims to provide a resilience-oriented planning strategy for community microgrids in Lombok Island, Indonesia. A mixed-integer linear program, implemented in the distributed energy resources customer adoption model (DER-CAM), is presented in this paper to find the optimal technology portfolio, placement, capacity, and optimal dispatch in a community microgrid. The multinode model is adopted for the planning, and hence, power flow constraints, N-1 contingency, and technology constraints are considered. The results show that the placement of photovoltaic (PV) arrays, battery energy storage systems (BESSs), and diesel generators (DGs) as backup sources in multi-node community microgrids lead to multiple benefits, including 100% rural electrification, over 25% cost savings, as well as over 22%, in particular CO2 emission reduction in multinode community microgrids.

Optimization of Solar Grid-Based Virtual Power Plant Using Distributed Energy Resources Customer Adoption Model: A Case Study of Indian Power Sector

Optimization of Solar Grid-Based Virtual Power Plant Using Distributed Energy Resources Customer Adoption Model: A Case Study of Indian Power Sector

Economic and Environmental Impacts of Distributed Energy Resources

The current power system suffers from inherent inefficiencies and transmission line congestion due to the spatial split between power generation and end usage. This potentially introduces shortcomings in meeting load demands, grid liability, renewable portfolio standards, and environmental considerations such as carbon emission reduction targets. The economic and technical viability of distributed energy resource (DER) technologies may accelerate the transition to more sustainable energy production. This paper investigates the economic and environmental benefits of DERs compared to utility prices and emissions for residential dwellings using the Distributed Energy Resources Customer Adoption Model (DER-CAM). The results show a tradeoff between the CO2 emissions and electricity costs, but improvements over purchasing the electricity.

A comprehensive techno-economic assessment of the impact of natural gas-fueled distributed generation in European electricity distribution networks

This paper combines three models:, an energy simulator (eQuest), the Distributed Energy Resources Customer Adoption Model (DER-CAM), and a Reference Network Model (RNM), to quantify the impact of natural gas-fueled distributed generation (NGDG) in electricity distribution networks. First, eQuest is used to determine the energy profile of each type of building. Then, DER-CAM determines the optimal planning and operation of distributed energy resources. Finally, the hourly profiles of all buildings are processed by RNM to assess the impact of NGDG integration in electricity distribution networks. The reason behind this multi-level approach is that prosumers manage distributed energy resources to maximize their benefits, and later these decisions have an impact upon the electricity distribution networks. We assess this impact. The RNM evaluates power flows to size the network components, and determines the new components required, quantifying the corresponding investments in terms of low and medium voltage network and medium to low voltage transformers. We use this methodology to analyze the sensitivity of network reinforcements to NGDG penetration. Six European networks for urban and semi-urban distribution areas in Germany, Italy, and France have been studied. The results show clear differences in the expected impact in each of these countries. They depend greatly on the level of NGDG penetration and selected network, with network costs ranging up to 150€ and savings of 900€ per building. Energy losses decrease for low NGDG penetration levels and can double at 100% NGDG penetration levels.

Improving estimates for reliability and cost in microgrid investment planning models

This paper develops a new microgrid investment planning model that determines cost-optimal investment and operation of distributed energy resources (DERs) in a microgrid. We formulate the problem in a bilevel framework, using particle swarm optimization to determine investment and the DER-CAM model (Distributed Energy Resources Customer Adoption Model) to determine operation. The model further uses sequential Monte Carlo simulation to explicitly simulate power outages and integrates time-varying customer damage functions to calculate interruption costs from outages. The model treats nonlinearities in reliability evaluation directly, where existing linear models make critical simplifying assumptions. It combines investment, operating, and interruption costs together in a single objective function, thereby treating reliability endogenously and finding the cost-optimal trade-off between cost and reliability—two competing objectives. In benchmarking against a version of the DER-CAM model that treats reliability through a constraint on minimum investment, our new model improves estimates of reliability (the loss of load expectation) by up to 600%, of the total system cost by 6%–18%, of the investment cost by 32%–50%, and of the economic benefit of investing 27%–47%. Improvements stem from large differences in investment of up to 56% for natural gas generators, solar photovoltaics, and battery energy storage.

The Impact of Short-Term Stochastic Variability in Solar Irradiance on Optimal Microgrid Design

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.

A Hybrid RES Distributed Generation System for Autonomous Islands: A DER-CAM and Storage-Based Economic and Optimal Dispatch Analysis

The possibility of replacing the existing autonomous thermal power plants by Distributed Energy Resources (DER) based on renewable energy sources (RES), along with the appropriate energy storage technologies in order to deal with the major problems that autonomous islands usually face was investigated. A case study of a small Greek island, which is confronted by various energy and water shortages, was studied for assessing the feasibility of DER deployment. The main objectives investigated were cost minimization, CO2 emissions minimization and DER reliability maximization. The DER-CAM (Distributed Energy Resources Customer Adoption Model) decision support tool was used for the multi-objective analysis conducted, which proposes a set of optimal solutions defining the appropriate Distributed Generation (DG) technologies, the capacities of storage and other technologies and the optimal dispatch of the DG system. A mutual beneficial solution, for all stakeholders, was proposed indicating the scope for developing such systems for all islands facing the same problems.

Optimal battery storage operation for PV systems with tariff incentives

Many efforts are recently being dedicated to developing models that seek to provide insights into the techno-economic benefits of battery storage coupled to photovoltaic (PV) generation system. However, not all models consider the operation of the PV – battery storage system with a feed-in tariff (FiT) incentive, different electricity rates and battery storage unit cost. An electricity customer whose electricity demand is supplied by a grid connected PV generation system benefiting from a FiT incentive is simulated in this paper. The system is simulated with the PV modelled as an existing system and the PV modelled as a new system. For a better understanding of the existing PV system with battery storage operation, an optimisation problem was formulated which resulted in a mixed integer linear programming (MILP) problem. The optimisation model was developed to solve the MILP problem and to analyse the benefits considering different electricity tariffs and battery storage in maximising FiT revenue streams for the existing PV generating system. Real data from a typical residential solar PV owner is used to study the benefit of the battery storage system using half-hourly dataset for a complete year. A sensitivity analysis of the MILP optimisation model was simulated to evaluate the impact of battery storage capacity (kWh) on the objective function. In the second case study, the electricity demand data, solar irradiance, tariff and battery unit cost were used to analyse the effect of battery storage unit cost on the adoption of electricity storage in maximising FiT revenue. In this case, the PV is simulated as a new system using Distributed Energy Resources Customer Adoption Model (DER-CAM) software tool while modifying the optimisation formulation to include the PV onsite generation and export tariff incentive. The results provide insights on the benefit of battery storage for existing and new PV system benefiting from FiT incentives and under time-varying electricity tariffs.

The impact of ancillary services in optimal DER investment decisions

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.

Evaluating business models for microgrids: Interactions of technology and policy

Policy makers are increasingly focused on strategies to decentralize the electricity grid. We analyze the business model for one mode of decentralization—microgrids—and quantify the economics for self-supply of electricity and thermal energy and explicitly resolve technological as well as policy variables. We offer a tool, based on the Distributed Energy Resources Customer Adoption Model (DER-CAM) modeling framework, that determines the cost-minimal capacity and operation of distributed energy resources in a microgrid, and apply it in southern California to three “iconic” microgrid types which represent typical commercial adopters: a large commercial building, critical infrastructure, and campus. We find that optimal investment leads to some deployment of renewables but that natural gas technologies underpin the most robust business cases—due in part to relatively cheap gas and high electricity rates. This finding contrasts sharply with most policy advocacy, which has focused on the potentials for decentralization of the grid to encourage deployment of renewables. Decentralization could radically reduce customer energy costs, but without the right policy framework it could create large numbers of small decentralized sources of gas-based carbon emissions that will be difficult to control if policy makers want to achieve deep cuts in greenhouse gas emissions.

Optimal distributed energy resources and the cost of reduced greenhouse gas emissions in a large retail shopping centre

Abstract This paper presents a case study on optimal options for distributed energy resource (DER) technologies to reduce greenhouse gas emissions in a large retail shopping centre located in Sydney, Australia. Large retail shopping centres take the largest share of energy consumed by all commercial buildings, and present a strong case for adoption of DER technologies to reduce energy costs and emissions. However, the complexity of optimally designing and operating DER systems has hindered their widespread adoption in practice. This paper examines and demonstrates the value of DER in reducing the carbon footprint of the shopping centre by formulating and solving a multiobjective optimisation problem using the Distributed Energy Resources Customer Adoption Model (DER-CAM) tool. An economic model of the shopping centre is developed in DER-CAM using on-site-specific demand, tariffs, and performance data for each DER technology option available. Four key optimal DER technology investment scenarios are then analysed by comparing: (1) solution trade-offs of costs and emissions, (2) the cost of reduced emissions attained in each investment scenario, and (3) investment benefits with respect to the business-as-usual scenario. The analysis shows that a moderate investment in combined cooling, heat and power (CCHP) technology alone can reduce annual energy costs by 8.5% and carbon dioxide-equivalent emissions by 29.6%. A larger investment in CCHP technology, in conjunction with on-site solar photovoltaic (PV) generation, can deliver nearly 72% reductions in emissions at a 47% increase in total energy costs. The study demonstrates the effectiveness and flexibility of this type of modelling approach for the analysis, design, and ongoing assessment of new DER technologies under changing market conditions, supporting a more robust adoption of DER technologies in the commercial building sector.

Optimizing Distributed Energy Resources and building retrofits with the strategic DER-CAModel

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 0.53W/(m2K) 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. Furthermore, the results obtained with DER-CAM illustrate the complexity of interactions between DER and passive measure options, showcasing the need for a holistic optimization approach to effectively optimize energy costs and CO2 emissions. The simultaneous optimization of building shell improvements and DER investments enables building owners to take one step further towards nearly zero energy buildings (nZEB) or nearly zero carbon emission buildings (nZCEB), and therefore support the 20/20/20 goals.

Modeling of thermal storage systems in MILP distributed energy resource models

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.

Modeling of Thermal Storage Systems in MILP Distributed Energy Resource Models - eScholarship

Modeling of Thermal Storage Systems in MILP Distributed Energy Resource Models I David Steen a,b , Michael Stadler a,c , Goncalo Cardoso a,d , Markus Groissbock a,c , Nicholas DeForest a , Chris Marnay a David Steen is an affiliate with Berkeley Lab, USA, and is with Chalmers University of Technology, Sweden Michael Stadler is with Berkeley Lab, USA, and leads the microgrid team at Berkeley Lab as well as the Center for Energy and Innovative Technologies, Austria Goncalo Cardoso is with Berkeley Lab, USA, and with Instituto Superior Tecnico - University of Lisbon, Portugal Nicholas DeForest is with Berkeley Lab, USA Markus Groissbock was an affiliate with Berkeley Lab, USA, and with the Center for Energy and Innovative Technologies, Austria Chris Marnay is a retired Staff Scientist and current affiliate with Berkeley Lab, and is President of Microgrid Design of Mendocino, USA a Lawrence Berkeley National Laboratory 1 Cyclotron Road MS 90R1121 Berkeley CA 94720 USA b Chalmers University of Technology Department of Energy and Environment SE-412 96 Goteborg, Sweden Phone: +46(0)31-772 16 63 c Center for Energy and innovative Technologies - CET Doberggasse 9 A-3681 Hofamt Priel Austria d Instituto Superior Tecnico - University of Lisbon Avenida Rovisco Pais 1, 1049-001 Lisboa Portugal Corresponding author e-mail: david.steen@chalmers.se, mobile: 0046-739169596 Second contact: MStadler@lbl.gov 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. The Distributed Energy Resources Customer Adoption Model (DER-CAM) has been designed at Lawrence Berkeley National Laboratory (LBNL). Furthermore, Chalmers Energy Initiative is greatly acknowledged for funding D. Steen’s guest research visit to Lawrence Berkeley National Laboratory. July 2014

Regional analysis of building distributed energy costs and CO2 abatement: A U.S.–China comparison

The following paper conducts a regional analysis of the U.S. and Chinese buildings’ potential for adopting Distributed Energy Resources (DER). The expected economics of DER in 2020–2025 is modeled for a commercial and a multi-family residential building in different climate zones. The optimal building energy economic performance is calculated using the Distributed Energy Resources Customer Adoption Model (DER-CAM) which minimizes building energy costs for a typical reference year of operation. Several DER such as combined heat and power (CHP) units, photovoltaics, and battery storage are considered. The results indicate DER have economic and environmental competitiveness potential, especially for commercial buildings in hot and cold climates of both countries. In the U.S., the average expected energy cost savings in commercial buildings from DER-CAMs suggested investments is 17%, while in Chinese buildings is 12%. The electricity tariffs structure and prices along with the cost of natural gas, represent important factors in determining adoption of DER, more so than climate. High energy pricing spark spreads lead to increased economic attractiveness of DER. The average emissions reduction in commercial buildings is 19% in the U.S. as a result of significant investments in PV, whereas in China, it is 20% and driven by investments in CHP.

Applications of optimal building energy system selection and operation

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

Special Issue on Challenges and Opportunities in the 21st Century Energy Infrastructure

Special Issue on Challenges and Opportunities in the 21st Century Energy Infrastructure

An Economic Analysis of Used Electric Vehicle Batteries Integrated Into Commercial Building Microgrids

Current policies in the U.S. and other countries are trying to stimulate electric transportation deployment. Consequently, plug-in electric vehicle (PEV) adoption will presumably spread among vehicle users. With the increased diffusion of PEVs, lithium-ion batteries will also enter the market on a broad scale. However, their costs are still high and ways are needed to optimally deploy vehicle batteries in order to account for the higher initial outlay. This study analyzed the possibility of extending the lifecycle of PEV batteries to a secondary, stationary application. Battery usage can be optimized by installing used battery packs in buildings' microgrids. Employed as decentralized storage, batteries can be used for a microgrid's power supply and provide ancillary services (A/S). This scenario has been modeled with the Distributed Energy Resources Customer Adoption Model (DER-CAM), which identifies optimal equipment combinations to meet microgrid requirements at minimum cost, carbon footprint, or other criteria. Results show that used PEV batteries can create significant monetary value if subsequently used for stationary applications.

Optimal Technology Selection and Operation of Commercial-Building Microgrids

The deployment of small ( < 1-2 MW ) clusters of generators, heat and electrical storage, efficiency investments, and combined heat and power (CHP) applications (particularly involving heat-activated cooling) in commercial buildings promises significant benefits but poses many technical and financial challenges, both in system choice and its operation; if successful, such systems may be precursors to widespread microgrid deployment. The presented optimization approach to choosing such systems and their operating schedules uses Berkeley Lab's Distributed Energy Resources Customer Adoption Model (DER-CAM), extended to incorporate electrical and thermal storage options. DER-CAM chooses annual energy bill minimizing systems in a fully technology-neutral manner. An illustrative example for a hypothetical San Francisco hotel is reported. The chosen system includes one large reciprocating engine and an absorption chiller providing an estimated 11% cost savings and 8% carbon emission reductions under idealized circumstances.

An analysis of the DER adoption climate in Japan using optimization results for prototype buildings with U.S. comparisons

This research demonstrates economically optimal distributed energy resource (DER) system choice using the DER choice and operations optimization program, the Distributed Energy Resources Customer Adoption Model (DER-CAM). DER-CAM finds the optimal combination of installed equipment given prevailing utility tariffs and fuel prices, site electrical and thermal loads (including absorption cooling), and a menu of available equipment. It provides a global optimization, albeit idealized, that shows how site useful energy loads can be served at minimum cost. Five prototype Japanese commercial buildings are examined and DER-CAM is applied to select the economically optimal DER system for each. Based on the optimization results, energy and emission reductions are evaluated. Significant decreases in fuel consumption, carbon emissions, and energy costs were seen in the DER-CAM results. Savings were most noticeable in the prototype sports facility, followed by the hospital, hotel, and office building. Results show that DER with combined heat and power equipment is a promising efficiency and carbon mitigation strategy, but that precise system design is necessary. Furthermore, a Japan–U.S. comparison study of policy, technology, and utility tariffs relevant to DER installation is presented.