Annual Energy Cost Research Articles

Assessment of Energy Efficiency Using an Energy Monitoring System: A Case Study of a Major Energy-Consuming Enterprise in Vietnam

Vietnam’s economy has been growing rapidly in the last 20 years, leading to significant increases in energy consumption as well as in carbon emissions. Most electricity is consumed by loads of industry and construction due to the country’s socio-economic development strategy. An energy saving strategy cannot be achieved if the industry factories lack energy consumption data. The installation of energy monitoring systems can help to improve energy efficiency by supplying daily, monthly, and yearly energy consumption reports. Moreover, major energy-consuming enterprises in Vietnam must implement solutions for energy-efficient use as prescribed in the Law on Energy Efficient Use. Therefore, this study aimed to determine the impact of an energy monitoring system as an improvement solution for energy efficiency in a typical major energy-consuming enterprise in Vietnam. The study’s results, after six months, show that the total saved electricity after installing the power monitoring system was 191,923 kWh. The company saved approximately 19.584 USD and reduced emission to the environment by 139 tons of CO2. In addition, the return on investment time of power monitoring systems is about 14 months, while the annual energy costs of the factory can be reduced by about 9.62% per year. Therefore, power monitoring systems should be promoted in factories with different scales to control energy wastage in the domestic industry field.

Simulation of the influence of STATCOM on power system losses

The supply of growing electricity demand is possible through continuous technological advances and the expansion of national and international electrical systems. This scenario could introduce voltage drops and consequent changes in the reactive power flow throughout the electrical network. In order to control these problems, various strategies have been developed as a solution to improve the transport and distribution of electrical energy. One of them is the Flexible Alternating Current Transmission System (FACTS), and more specifically the STATic synchronous COMpensator (STATCOM). This paper investigates the influence and effectiveness of STATCOM to mitigate the losses in the transmission lines and its impacts on bus voltage drops. The simulations are performed using the software DIgSILENT PowerFactory and the results showed that STATCOM reduces the power system losses in an interval of 23.86% until 32.86%, and in addition, the STATCOM decreases the annual energy cost by 7.82% in the implemented test case.

BIM-based architectural analysis and optimization for construction 4.0 concept (a comparison)

The growing need for electricity has put Pakistan's burgeoning economy in peril. The notion of “Construction 4.0″ is considered in this study since it enables the greatest utilization of energy and architectural analysis. A case study and a method for building information modelling are used to analyze the concepts of green building. The case study building is represented as a parametric model using the Autodesk Revit platform with the original blueprints and data. Using Autodesk Insight 360, an energy analysis and comparison of optimization case study of the A-Block and Z-Block COMSATS Abbottabad, Pakistan is chosen. This study analyses an academic building's energy performance as a case study to reduce energy usage. By turning the building 360 degrees at 45-degree intervals and utilizing BIM to install energy-efficient construction materials, this study analyses the energy efficiency of an academic building. The average annual energy cost for blocks A and Z is decreased from 228 kWh/m2 to 160 kWh/m2 and 192 kWh/m2, respectively.

An intelligent design and environmental consideration of a green-building system utilizing biomass and solar having a bidirectional interaction with the grid to achieve a sustainable future

As a result of climate change and environmental pollutants becoming more prevalent in the environment, this paper introduces an innovative approach to energy systems that uses smart building technologies and renewable energy sources to mitigate climate change and promote sustainability. The central concept of this method is to hybridize solar and biomass resources in order to meet the heating, cooling, and electricity needs of a residential neighborhood over the year. The system is driven by an intelligent design of photovoltaic thermal panels combined with an efficient heater. This idea reduces the solar system cost and increases the independency from the local heating and cooling networks when the sun is unavailable. Besides, the waste heat recovery process is introduced to utilize the additional heat to charge the chiller and space heating demand and avoid releasing excess heat into the environment as much as possible. Multiple controllers are employed to launch a bidirectional connection with the electricity grid to sell or buy energy to or from the grid based on production, demand, and usage. The system’s affordability, effectiveness, and environmental sustainability are assessed comprehensively via TRNSYS software and MATLAB-developed code. According to the results, the proposed smart system achieves a very low emission rate of 14 kg.CO2/MWh with an affordable energy cost of 76.3 $/MWh. The results further reveal that while the system chiefly depends on biomass in cold hours to meet the heating demand, most of the building’s cooling need is provided by the photovoltaic thermal panels in summer, signifying the importance of renewable energy resources combination. It is noteworthy that due to the combination of green energy sources, the amount of carbon dioxide emission is significantly reduced by 35%. In addition, the net electricity is positive 2300 h of the year, meeting the demand, charging the chiller and tank, and seeling the additional to the grid, offsetting a significant portion of the annual energy costs. The results eventually showed that while the minimum energy cost of 79 $/MWh was obtained in April, the performance efficiency and emission rate reached the maximum and minimum values of 41.5% and 10.5 kg.CO2/MWh in December.

BIM to BEM Transition for Optimizing Envelope Design Selection to Enhance Building Energy Efficiency and Cost-Effectiveness

This article focuses on developing an energy simulation model through Building Information Modeling (BIM) and Building Energy Modeling (BEM)to optimize energy in building design in Vietnam. Reducing the energy consumption in buildings will help reduce operating costs, impact the environment, and increase the efficiency of buildings. However, there is limited research on buildings with complex structures and configurations, detailed surface design, and envelope construction, especially in simulating details through BIM. The author proposes converting from BIM to BEM to simulate energy in buildings and optimize the factors related to building construction in the envelope design of the building. These factors include wall cladding materials, mirror materials, the window ratio on the walls, and other details. This study has effectively created an energy model for a public building, allowing for the calculation of the Energy Intensity Index (EUI) and annual energy costs for various scenarios. By altering factors associated with the design and construction process, the system has the potential to decrease both energy intensity and usage costs for the building. The study results will help designers and building managers improve and enhance energy efficiency in building projects.

Operational optimization for the grid-connected residential photovoltaic-battery system using model-based reinforcement learning

The development of distributed photovoltaic and energy storage devices has created challenges for energy management systems due to uncertainty and mismatch between local generation and residents' energy demand. Reinforcement learning is gaining attention as a control algorithm, but traditional model-free RL has data quality and quantity limitations for energy management applications. Therefore, this study proposed a model-based deep RL method to optimize the operation control of the energy storage system by taking the measured dataset of an actual existing building in Japan as the research object. With an optimization goal of reducing the microgrid's energy cost and ensuring the PV self-consumption ratio, we designed a new reward function for these goals. We took the benchmark strategy currently used by the target building's energy management system as the baseline model in the experiment. We applied four advanced RL algorithms (PPO, DQN, DDPG, and TD3) to optimize the baseline model. The results show that the proposed RL design can better achieve the two optimization objectives of minimizing energy cost and maximizing the PV self-consumption ratio. Among them, the TD3 algorithm presented the best performance. Compared with the baseline model, its annual energy cost can be reduced by 17.82%, and the photovoltaic self-consumption ratio can be increased by 0.86%. In addition, the model-based RL method proposed in this paper can provide a better energy management strategy with the training set of only one and a half years of measured data, which proves that it has a high potential for practical application.

An Optimization-Based Model for A Hybrid Photovoltaic-Hydrogen Storage System for Agricultural Operations in Saudi Arabia

Renewable energy technologies and resources, particularly solar photovoltaic systems, provide cost-effective and environmentally friendly solutions for meeting the demand for electricity. The design of such systems is a critical task, as it has a significant impact on the overall cost of the system. In this paper, a mixed-integer linear programming-based model is proposed for designing an integrated photovoltaic-hydrogen renewable energy system to minimize total life costs for one of Saudi Arabia’s most important fields, a greenhouse farm. The aim of the proposed system is to determine the number of photovoltaic (PV) modules, the amount of hydrogen accumulated over time, and the number of hydrogen tanks. In addition, binary decision variables are used to describe either-or decisions on hydrogen tank charging and discharging. To solve the developed model, an exact approach embedded in the general algebraic modeling System (GAMS) software was utilized. The model was validated using a farm consisting of 20 greenhouses, a worker-housing area, and a water desalination station with hourly energy demand. The findings revealed that 1094 PV panels and 1554 hydrogen storage tanks are required to meet the farm’s load demand. In addition, the results indicated that the annual energy cost is $228,234, with a levelized cost of energy (LCOE) of 0.12 $/kWh. On the other hand, the proposed model reduced the carbon dioxide emissions to 882 tons per year. These findings demonstrated the viability of integrating an electrolyzer, fuel cell, and hydrogen tank storage with a renewable energy system; nevertheless, the cost of energy produced remains high due to the high capital cost. Moreover, the findings indicated that hydrogen technology can be used as an energy storage solution when the production of renewable energy systems is variable, as well as in other applications, such as the industrial, residential, and transportation sectors. Furthermore, the results revealed the feasibility of employing renewable energy as a source of energy for agricultural operations.

Optimal sizing of RES and BESS in networked microgrids based on proportional peer‐to‐peer and peer‐to‐grid energy trading

AbstractNetworked microgrids emerged from the growing deployment of microgrids in the distribution network. The coordinated operation of networked microgrids offers technical and economical benefits to microgrid owners, customers, grid/utility, and other stakeholders. The optimal capacity sizing of renewable energy sources and battery energy storage systems allows microgrids to minimize costs and maximize reliability. A multi‐objective optimization problem is developed for optimal sizing in a networked microgrid consisting of four different microgrids. The annual energy costs and loss of power supply probability index are taken as objectives. Peer‐to‐peer and peer‐to‐grid energy trading approaches are employed. The peer‐to‐peer energy trading among microgrids employs the proposed “proportional trading method” via a networked microgrid manager or aggregator. The multi‐objective optimization problem formulated is solved using Multi‐Objective Particle Swarm Optimization. The individual objective optimization results for annual energy cost and loss of power supply probability are also analyzed. The proposed method decreases the interaction between the grid and the MGs, and the usage of renewable energy sources is enhanced. The capacity of battery energy storage systems is lowered by 96%, 53.2%, 48.86%, 21% for respective microgrids in networked microgrids. The results of proportional peer‐to‐peer energy trading‐based multi‐objective optimization show that trading energy among microgrids minimizes annual energy cost by 0.75% while maintaining system reliability.

Maximizing Energy Performance of University Campus Buildings through BIM Software and Multicriteria Optimization Methods

University buildings have high energy requirements due to their size, numerous users, and activities, which considerably contribute to environmental contamination. Implementing energy-saving solutions in these structures has a favorable influence on the economics and the conservation of energy resources. A higher education building’s energy behavior can be simulated using software to identify the optimal strategies that result in energy savings. In this research, Autodesk Revit, Autodesk Insight, and Green Building Studio are among the programs utilized to examine the energy efficiency of the university building in four European cities. Following the development of several energy-saving scenarios for the building, the offered solutions are evaluated based on their annual energy consumption, energy costs, and CO2 emissions. Finally, multicriteria analysis techniques such as the AHP and PROMETHEE are applied to choose the best scenario for each instance. The study’s findings indicate that the ASHRAE Terminal Package Heat Pump scenario performed well in all of the cities examined, reducing yearly energy usage by 43.75% in Wien and annual energy costs by 47.31% in Mallorca. In comparison, the scenario utilizing a high-efficiency VAV system with a gas boiler and chiller came in last in all situations, resulting in a decrease of 12.67% in Mallorca’s annual energy usage and a reduction of 17.57% in Palermo’s annual energy expenses.

Assessment of Energy Consumption of Brine Discharge from SWRO Plants

The San Pedro del Pinatar I and II desalination plants in Spain were constructed near Posidonia oceanica meadows protected at the national and European level. The environmental impact statement for these plants stipulate that the brine discharge from the plant must not impact the meadows. To this end, a 4790 m submerged outfall was constructed to bypass the lower limit of the seagrass meadows, and a diffuser piece, along with an outfall pumping system, was installed at the end of the outfall. The objective of this paper is to evaluate the economic cost of the energy consumed for the brine discharge evacuation process necessary to comply with environmental requirements. The operating time and power consumption data were obtained from the plant’s monitoring system, while the energy cost was obtained from energy invoices. The computed results show that it is possible to minimize the environmental impacts of brine discharge on the marine environment of an SWRO plant with a low economic cost. The average energy consumption of the reject effluent pumping system ranged from 19.4 to 1239.3 thousand kWh per year, while the average annual energy cost was 49,329 €, which amounts to only 0.56% of the total energy cost for plant operation. The adoption of these measures provide a cost-effective means to meet environmental protection requirements and minimize the environmental impact associated with the discharged brine. As the demand for desalination operations increase, economically and scientifically viable technologies for mitigating environmental impacts are necessary for sustainability in this domain.

Optimal Inverter-Based DG Placement considering Energy Loss, Reliability, Voltage Sag, DG, and Substation Power Cost Issues

This article presents a comprehensive multi-objective-based optimization problem formulation for finding the optimum size, site, and number of inverter-based DGs (IBDGs) for the operation of the distribution system so as to minimize annual energy cost comprising of costs for the energy loss during the normal operating state, running the IBDGs, substation power including penalty for reverse power flow (if any) and the average energy not supplied as a measure of reliability. Besides, the cost of average apparent load disturbed as a result of voltage sags during faults is also considered. The method is tested on the 33-bus distribution test system using particle swarm optimization. The results so obtained, demonstrate the effectiveness and applicability of the proposed formulation to find the optimum location(s), size(s), and number of IBDG(s) in the radial distribution system with appropriate constraints consideration.

An Economic Analysis of Energy Consumption at Student Residences in a South African-Based Academic Institution Using NARX Neural Network

One of the issues associated with the supply of electricity is its generation capacity, and this has led to prevalent power cuts and high costs of usage experienced in many developing nations, including South Africa. Historical research has shown that the annual rate of increase for electricity has grown at an alarming rate since 2008 and, in some years, has grown as much as 16%. The objectives of this study are to estimate the cost analysis of electricity usage at the twenty-nine residences of the University of Johannesburg (UJ-Res) and propose a model for our university, as well as other South African universities, to become more energy-efficient. This was achieved by analyzing the tariffs between 2015 and 2021. A forecast was made for a period of five years (2021 to 2026) using a non-linear autoregressive exogenous neural network (NARX-NN) time-series model. From the results obtained, the better NARX-NN model studied has a root mean squared error (RMSE) of 2.47 × 105 and a determination coefficient (R2) of 0.9661. The projection result also shows that the annual cost of energy consumed will increase for the projected years, with the year 2022 being the peak with an estimated annual cost of over ZAR 30 million (USD 2,076,268).

Prosumer-centric demand side management for minimizing electricity bills in a DC residential PV-battery system: An Australian household case study

Technological advancements lead to the development of a home energy management system (HEMS) to perform demand side management (DSM) in residential houses. The current study proposes a DSM strategy to minimize electricity bills in a DC residential house considering the predictions of electrical load consumption and solar photovoltaic (PV) power based on long short-term memory networks. A simulation study compares the proposed DSM strategy against four alternate scenarios, the result shows that the proposed DSM strategy has the lowest annual energy cost increment (5.81%) compared to the benchmark scenario where it is assumed to have the ideal predictions of the load consumption and solar PV power. It also shows that the predictions of the load consumption and solar PV power are essential for economically sensible DSM, however further analysis reveals that the prediction errors do not correlate with the differences in daily energy costs (or daily peak powers) resulting from the proposed DSM strategy and benchmark scenario. Finally, the proposed DSM strategy is experimentally validated on a small-scale hardware-in-the-loop test platform to demonstrate the feasibility of its practical implementation in a HEMS.

Coordinated energy management for commercial prosumers integrated with distributed stationary storages and EV fleets

The integration of intermittent renewable energy and stochastic electric vehicle (EV) loads in buildings and power systems poses new challenges such as increased peak demands and grid instability. Regarding multi-directional interactions among heterogeneous loads, generation, storages, and power grid, the synergy between prosumers, stationary storages and electromobility is crucial for future energy systems. In this study, coordinated energy management was investigated for a neighborhood involving commercial buildings with distributed renewables and stationary batteries, and plug-in EVs. Compared to the collaborative control of EV and stationary storages at single prosumers, the energy sharing system with centralized control of mobile and stationary storages achieved superior techno-economic performance. Case studies demonstrated that the renewables-storage sharing with centralized storage scheduling decreased annual energy costs for both buildings and EV owners by over 15%, and the cycle aging cost of stationary storage by 25%. While the performance under day-ahead scheduling of EV and stationary storages depends more on the prediction accuracy, the real-time renewables-storage sharing strategy integrated with pre-scheduled EV charging provides an effective alternative solution for the stakeholders. The study explores potential coordinated approaches for communities with low-carbon buildings, flexible storages, and intelligent infrastructure to maximize the operational value and improve the grid stability.

Multi-Criteria Energy Management with Preference Induced Load Scheduling Using Grey Wolf Optimizer

Minimizing energy costs while maintaining consumer satisfaction is a very challenging task in a smart home. The contradictory nature of these two objective functions (cost of energy and satisfaction level) requires a multi-objective problem formulation that can offer several trade-off solutions to the consumer. Previous works have individually considered the cost and satisfaction, but there is a lack of research that considers both these objectives simultaneously. Our work proposes an optimum home appliance scheduling method to obtain an optimum satisfaction level with a minimum cost of energy. To achieve this goal, first, an energy management system (EMS) is developed using a rule-based algorithm to reduce the cost of energy by efficient utilization of renewable energy resources and an energy storage system. The second part involves the development of an optimization algorithm for optimal appliance scheduling based on consumer satisfaction level, involving their time and device-based preferences. For that purpose, a multi-objective grey wolf accretive satisfaction algorithm (MGWASA) is developed, with the aim to provide trade-off solutions for optimal load patterns based on cost per unit satisfaction index (Cs_index) and percentage satisfaction (%S). The MGWASA is evaluated for a grid-connected smart home model with EMS. To ensure the accuracy of the numerical simulations, actual climatological data and consumer preferences are considered. The Cs_index is derived for six different cases by simulating (a) optimal load, (b) ideal load, and (c) base (random) load, with and without EMS. The results of MGWASA are benchmarked against other state-of-the-art optimization algorithms, namely, binary non-dominated sorting genetic algorithm-2 (NSGAII), multi-objective binary particle swarm optimization algorithm (MOBPSO), Multi-objective artificial bee colony (MOABC), and multi-objective evolutionary algorithm (MOEA). With the proposed appliance scheduling technique, a % reduction in annual energy cost is achieved. MGWASA yields Cs_index at 0.049$ with %S of 97%, in comparison to NSGAII, MOBPSO, MOABC, and MOEA, which yield %S of 95%, 90%, 92%, and 94% at 0.052$, 0.048$, 0.0485$, and 0.050$, respectively. Moreover, various related aspects, including energy balance, PV utilization, energy cost, net present cost, and cash payback period, are also analyzed. Lastly, sensitivity analysis is carried out to demonstrate the impact of any future uncertainties on the system inputs.

Calculation of the level of energy efficient heat protection

Introduction. The choice of the level of heat protection of buildings should be based on the economic performance of buildings, taking into account the cost of maintaining the pre-set thermal microclimate on the premises. Hence, it is necessary to initially determine the amount of energy consumed by the heating, atmospheric and mechanical cooling of buildings. Towards this end, buildings that are different in size and have different numbers of storeys are studied. These buildings are located in various Russian regions featuring different periods of heating and solar radiation intensity. They are Moscow (central Russia), Astrakhan (southern region) and Vorkuta (northern region).
 Materials and methods. The problem is solved computationally; the authors use buildings of different geometry that are located in Moscow, Astrakhan and Vorkuta. Various levels of heat protection of external walls and coatings, as well as different values of heat gain are considered. For all options, annual energy costs are calculated using primary fuel costs. Simulation and comparison were chosen as the main research methods.
 Results. The results are presented in the form of tables of annual primary energy consumption required to maintain the pre-set thermal parameters for various building options featuring different levels of heat protection and heat gain.
 Conclusions. According to the research findings, the highest level of heat protection is the best heat protection option from the standpoint of energy consumption in the cities under consideration (Moscow, Astrakhan and Vorkuta). Year-round heat gains reduce the load on the heating system during cold seasons; however, they have a negative impact du­ring warm seasons and transition periods due to the additional load on the air conditioning system. According to the calculation results, if the building heat density equals or exceeds 40 W/m2, some rooms may need cooling during cold seasons.

Economic comparison of cool and grey roofs of a small office building in the USA

EnergyPlus V9.0 simulations of a typical small office building prototype in five USA cities (Albuquerque, Atlanta, Buffalo, San Diego, Rochester) indicate that adding a cool roof (albedo 0.6) for an aged grey roof (albedo 0.2) would reduce the annual energy and annual energy cost in all hot dry or humid cities (Albuquerque, Atlanta, San Diego) and in severe cold or mixed humid cities (Buffalo, Rochester). The CO2, SO2, and NOX savings are positive in all selected cities. This work considers the economics of using cool roofs of an office building in the USA by performing a 20-year life-cycle cost analysis that compares each type of roof to a grey roof. The 20-year net present values (NPVs) of annual conditioning (heating + cooling) energy cost savings were calculated. The life cycle cost savings (NPV of annual streams – initial cost premium) of cool roofs have both positive and negative values. The NPV with the cool roofs is from -17.3 USD/m2 in Rochester to 6.5 USD/m2 in San Diego. Among the five cities in the USA, Albuquerque, Atlanta, and San Diego were able to apply the cool roofs since they all have positive NPV, while Buffalo and Rochester were unsuitable to apply the cool roofs methods because of the negative NPV. Owners concerned with urban heat island mitigation and slowing climate change may prefer cool roofs

Study on Energy Efficiency Using Building Information Modelling (BIM) at Shared Library Pagoh Higher Education Hub

Structures in Malaysia, particularly commercial buildings, have been exceeding the benchmark of building energy usage over the past few years. By maintaining thermal comfort of building under hot climate in Malaysia, air-conditioning system became a basic demand which incessantly operating to fulfil comfort requirement for the occupants in the library. Regards to this, research on energy efficiency using Building Information Modelling (BIM) software by simulating energy analysis was carried out in order to increase energy efficiency of the library. Mineral wool and Polyurethane Foam (P.U.F.) are the materials which proposed to apply in existing wall of building as the thermal insulation layer to increase thermal resistance when external heat transfer through the walls. The Objective of the study is to determine the existing building energy consumption and compare with energy analysis of mineral wool and polyurethane foam. The findings of the building energy analysis simulated by Green Building Studio (GBS) are then compared to discover the most appropriate walls for reducing the building’s energy consumption. P.U.F with its characteristics of high R-value and low U-value, obtain the most effective energy analysis which provide benefits of lowering total annual energy consumption of 294,651 , total annual energy cost of RM 107,645, and energy use intensity (EUI) of 121.7.

Simultaneous design and control optimization of smart glazed windows

The paper examines the benefits of simultaneous optimization of various design and control parameters for smart glazing windows of office buildings. The design variables include the window U-value, solar heat gain coefficient (SHGC), visible transmittance (Tvis), and window-to-wall ratio (WWR). The tint-switching control strategies reviewed include outdoor air temperature (OAT), indoor air temperature (IAT), incident total solar radiation (ITSR), and incident beam solar radiation (IBSR). These variables are optimized independently and simultaneously to determine the best specifications to minimize operating costs while maintained desired thermal and visual comfort levels. Specifically, electrochromic (EC) smart glazed windows are optimally designed and controlled when installed for a prototypical medium office building in Denver, Colorado. The optimization analysis results are considered to determine the breakeven installation costs required for smart glazing systems to be cost competitive compared to their static glazing counterparts. The analysis found that the annual energy cost savings from optimally designed and controlled smart glazed windows ranged from 11% − 18%. Of the design parameters, the U-value proved to be the most impactful on energy and cost reductions, followed by SHGC and WWR. Indoor and outdoor air temperature-based controls are determined the most effective operation strategies. Simultaneously optimizing for the design and control parameters provided the highest annual savings.

Operational planning of distributed energy generation for a semiconductor fabrication plant

The semiconductor industry is well known for its high resource consumption due to its clean room usage and power-intensive manufacturing technology. This is a significant financial burden for semiconductor fabrication plant owners who wish to minimize the cost needed to cover the electricity and gas loads for their daily operations. From a historical perspective, those electricity and gas cost reductions have been enabled by more energy-efficient manufacturing technologies. Due to the complexity of factors influencing the energy system of a semiconductor plant, other aspects have often been neglected and also not been studied intensively in the academic literature so far. One possibility for reducing energy costs is the integration of a microgrid as on-site distributed energy resources (DER) may offer a less cost-intensive way to supply the energy needed. Therefore, this paper seeks to answer the following research question: How much expected cost can be saved and risk mitigated at a semiconductor fabrication plant via DER? More precisely, in this paper, a microgrid at a semiconductor fabrication plant that has installed DER with combined heat and power (CHP) applications and demand response (DR) is used to simulate the expected annual energy costs under given uncertain electricity and gas prices at different installed capacity levels. A simulation-based approach is used to evaluate several DER capacity sizes. The results of the simulation show that installations of DER at a semiconductor microgrid can dominate the base case of doing nothing, considering the expected minimized cost, the expected CO2 emissions, and conditional value-at-risk (CVaR). In fact, DER can reduce the expected annual energy bill by up to 6%, whereas annual expected CO2 emissions can even be reduced by up to 22%. Moreover, with DER, a microgrid’s risk is reduced as it can react to market conditions and local demand. Additionally, the true potential of microgrid cost savings can only be enabled when DR is allowed and proves particularly effective when energy prices are more volatile.