1. Introduction
In recent years, electric power system design has witnessed a paradigm shift from the traditional centralized grid system toward decentralized and independent systems called microgrids, which can operate in either the grid-connected or islanded mode to supply demand targets. Researchers have increasingly been focusing on microgrids because they offer obvious advantages, such as high penetration of renewable energy resources (RESs), improved reliability due to the autonomous operation of distributed energy resources (DERs), and significant reductions in greenhouse gas emissions as well as reductions in a power system’s operating costs. Specifically, the optimal design of a microgrid considering economic and dynamic performance analysis was presented in reference [
1], which contains photovoltaics (PV), wind turbines, a battery energy storage system (BESS), and two diesel generators. The study indicated important benefits by microgrid adoption such as minimization of levelized cost of energy (LCOE), maximization of renewable energy penetration and significant reductions in fuel consumption. The technical benefits of energy storage system (ESS) utilization in a microgrid was determined in reference [
2], which included reducing peak load, providing standby power, and enhancing power quality. Similarly, the advantages of a microgrid comprising PV and ESS was indicated in reference [
3]. Regarding the ability of a microgrid to reduce emissions, an optimal microgrid design considering net zero emissions as an environmental constraint was presented in reference [
4]. The study in reference [
5] also highlighted significant reductions in carbon dioxide emissions by a microgrid, including biomass energy for rural electrification. The dynamic operation, transient stability, power efficiency and control of microgrid systems has been well developed and evaluated in the literature. The research presented in reference [
6] examined the dynamic operation and control strategies for a microgrid hybrid power system, and an intelligent controller for maximum power point tracking (MPPT) control was proposed, which enables the hybrid generation system to effectively extract the maximum power. The optimal operation of microgrids using multi-agent system was presented in reference [
7], which considered changes in the microgrid configuration due to the addition or removal of an energy unit. In reference [
8], the transient stability in a hybrid power system was improved using a novel intelligent damping controller, which was designed for the static synchronous compensator to reduce the power fluctuation, voltage support, and damping in the system operation. In addition, the fault analysis and protection methods for microgrid distribution systems were proposed considering various types of fault [
9,
10]. In summary, it is clear that the technical impact and operational feasibility of microgrid systems have been convincingly validated.
In the design stage of a microgrid system, it is fundamental to perform a techno-economic analysis, which helps determine whether a particular microgrid configuration is technically and financially feasible [
11]. From an economic perspective, one of the challenges in the integration of renewable energy technologies is their higher investment capital compared to conventional technologies [
12,
13]. To overcome this problem, technological innovation plays an important role in substantially reducing the installation costs of DERs and energy storage technologies [
14,
15]. In addition, many countries have launched financial incentive programs to promote the adoption of renewable energy systems. Numerous previous studies have evaluated the economics of microgrids considering real market conditions and policies, and the most recent studies are reported in this section. In reference [
16], the economics of local energy provision was examined considering market conditions in Southern California to determine the optimal configuration for different microgrid business models. The optimal selection and sizing of DERs were based on the cost savings and economic benefits of self-supply by microgrid adoption. However, demand-side flexibilities and renewable energy incentives, which directly impact energy savings and enhance the benefits of microgrid design, were not included in the analysis. In reference [
17], an optimization model based on mixed integer linear programing (MILP) for BESS operation in conjunction with PV modules was proposed to determine the optimal configuration with the objective function of maximizing revenue streams under feed-in tariff, which was the only incentive included in the study. The analysis presented in reference [
18] investigated the economic benefits of the optimal location of DERs in a microgrid, and the cost reduction was addressed in the absence of any capital grants. The economic analyses of microgrids in references [
16,
17,
18] were performed using the Distributed Energy Resources Customer Adoption Model (DER-CAM) modeling framework, which uses before-tax cash flow calculations. In references [
19,
20], photovoltaic-battery storage systems were studied with their own proposed energy dispatch schedule optimization, and system economics were quantified based on net present value (NPV) of the battery without addressing the benefits of incentives for PV and batteries, which directly improve cash inflows. In reference [
21], an economic analysis for planning purposes was conducted considering incentives and taxes, but this analysis did not solve the optimal sizing problem for DERs. In summary, these studies have the following limitations: They did not investigate the financial feasibility of microgrids; they did not include incentives in their economic analysis, which would substantially enhance system profitability; and/or they conducted analyses using before-tax calculations, which ignore the benefits arising from renewable energy tax credits and tax deductions. The research presented in reference [
22] highlighted that the impact of tax benefits can vary considerably from one microgrid configuration to another. Therefore, comparing alternatives on an after-tax basis is imperative for valid economic analysis.
In this study, a method for determining the optimal design of microgrids based on financial feasibility analysis is presented with the aim of maximizing project profitability. In addition to the economic benefits derived by local energy provision, this method incorporates all revenues from renewable energy incentives, tax credits, and tax deductions by performing analysis on after-tax cash flows. The technique was applied to obtain the optimal design for a campus microgrid in California, U.S. The primary reason why California was selected as a case study is because of its diversity of incentive programs for renewable energy and energy efficiency projects [
23]. The technologies considered for the campus microgrid include PV, ESS, and fuel cell (FC) because they are eligible for various incentive programs offered by the state and federal government. The incentives evaluated in this study are energy investment tax credit (ITC), investment-based incentive (IBI), net metering, depreciation and property tax incentive. In addition, the analysis considered the potential economic benefits of implementing load control with demand side management (DSM) in the microgrid, which contributes to energy savings. Finally, a sensitivity analysis of parameter uncertainties and incentive rates was conducted to evaluate their impact on the optimal configuration and financial feasibility of the microgrid. It was found that the optimal microgrid configuration resulted in reductions in electricity costs by 49.21%, and the project profitability was greatly enhanced when after-tax cash flow was used to quantify the tax benefits for renewable energy technologies. The results also indicated that financial incentives significantly affect the optimal microgrid design including both the optimal mix and sizing of DER units. Therefore, incorporating grant incentives and tax benefits in the economic evaluation of microgrids is crucial to have an effective design.
All of the simulations in the present study were conducted using MDSTool developed by us for the techno-economic analysis of hybrid renewable energy systems [
22]. MDSTool was created with the purpose of evaluating microgrid business models, which can address all of the economic benefits of a microgrid project. The tool is based on an open architecture implemented in the MATLAB programing environment, which offers several advantages compared to other commercial tools such as HOMER, iHOGA, HYBRID2 and SAM [
22]. Due to its open architecture, users can define and develop new dispatch algorithms, while assumptions and constraints can be modified and added, and technology models can be changed. In addition, a detailed financial structure specific to a region or country can be incorporated. The most important feature of MDSTool is that it provides both pre-tax and after-tax cash flow analysis, and is designed to incorporate any types of incentive and credit for different renewable energy technologies in a microgrid project.
The remainder of this paper is organized as follows.
Section 2 describes the structure of MDSTool and the microgrid design methodology arising from its application.
Section 3 summarizes the data of a case study performed in this paper.
Section 4 presents the results, discussion, and further analyses for the case study. Finally, our conclusions are provided in
Section 5.
2. Microgrid Design with MDSTool
MDSTool was developed to solve design and planning problems associated with hybrid renewable energy systems. The tool is used to determine the optimal configuration of a microgrid, which considers the optimal DER mix, DER capacity, and DER dispatch strategy, based on techno-economic analysis, which calculates all of the costs incurred by, and benefits derived from, a project over its lifetime. The structure of MDSTool is described in
Figure 1. It comprises two computation models, which are a performance model and an economic model. The performance model simulates the system operation optimization for one year, and records all of the energy generation and consumption profiles, which are then used as inputs for the economic model. Subsequently, the economic model computes all cash flows over the lifetime of the project to determine economic measures required for optimal DER sizing and financial feasibility analysis. A detailed description of MDSTool can be found in reference [
22].
2.1. Performance Model
The performance model simulates one-year power generation and consumption for each microgrid configuration in a search space which contains all of the possible capacities and combinations of DERs defined by the user. The inputs for the performance model include 8760-h time-series data of electricity load demands and weather resources, such as solar irradiance and wind speed, the technical specifications and cost parameters of DERs and a user-defined economic dispatch algorithm. The output is a set of time-series data which details hourly energy production and consumption within the system. The mathematical modeling of DERs is the primary determinant of the accuracy of this model. MDSTool employs time-series behavioral models used for long-term performance prediction and evaluation. Currently, the technologies modeled in MDSTool consist of PV, wind turbines, geothermal energy, biomass, internal combustion engines, FCs and energy storage. As the microgrid used as a case study in this paper contains a PV, a BESS, and an FC, their mathematical models are presented here.
2.1.1. PV Model
PV modules are rated on the basis of the power generated under the standard testing condition (STC) of 1 kW/m
2 of sunlight and a PV cell temperature of 25 °C. Various performance models for PV modules were developed [
24,
25]. The power produced by PV modules can be calculated as a function of solar irradiance, as given in Equation (1).
2.1.2. BESS Model
MDSTool uses the battery model developed by Manwell and McGowan [
26], which is based on the concept of electrochemical kinetics, and can determine the charge and discharge power from a battery at each time step. The maximum amount of power that a battery can discharge and charge over time step Δt [h] is given by Equations (2) and (3), respectively.
A power electronic converter is modeled as a black box and the modeling is undertaken using an efficiency, which reflects the power loss between the input and output.
2.1.3. FC Model
FC is considered to be generator with generation cost model using fuel cost curve, in which the fuel consumption as a function of the electrical output power is assumed to be linear as in reference [
27]:
2.1.4. DER Dispatch Model
To perform the system operation optimization, a dispatch algorithm for DERs must be specified. This dispatch algorithm involves the problem of unit commitment and economic dispatch of DERs. At each time step, the algorithm decides which components will operate and at what power level to efficiently supply the load demand at a minimum operating cost while satisfying operational constraints. The objective function is described by Equation (5) followed by its constraints.
subject to the following constraints:
Equation (6) describes the energy balance constraint between energy demand and supply at each time step. The set of Equation (7) shows the inequality constraints controlling the output power of the energy sources, as well as the purchase from and sale to the grid to satisfy the maximum and minimum thresholds. Equation (8) indicates that the SoC of storage units must be maintained between the maximum and minimum values.
The optimization of system operation is performed at each time step, and this is repeated 8760 times to complete the one-year operation of a configuration. Subsequently, the performance model decides whether a configuration is technically feasible based on reliability constraints. MDSTool utilizes the two most widely used reliability metrics: Loss-of-load probability (LOLP) and loss-of-power supply probability (LPSP) [
28]. As a result, the energy generation and consumption profiles of feasible configurations are saved and used as the inputs for the economic model, which calculates all cash flows for financial feasibility and optimal sizing analyses.
2.2. Economic Model
MDSTool uses cash flow analysis, which accounts for all expenses (cash outflows) and benefits (cash inflows). The pre-tax cash flow calculates the total installation and operating costs, and all project revenues, while the after-tax cash flow includes all debt payments and income tax considering tax benefits from debt interest payments, depreciation and tax credits such as ITC and production tax credit (PTC) [
22].
The pre-tax cash outflows and inflows are calculated using Equations (9) and (10), respectively. The only pre-tax cash outflow in year zero is the project equity, while the cash outflows in subsequent years account for all costs incurred over the project’s lifetime. The pre-tax cash inflow in year zero is equal to any IBI. In subsequent years, the pre-tax cash inflows are all project revenues.
where
The total pre-tax cost in each year is the net difference between pre-tax cash inflows and cash outflows. The total pre-tax cash flow is calculated by adding the benefits from energy savings to the pre-tax cost.
In the after-tax basis, the total after-tax cost is determined by subtracting the total debt payment and income tax from
FPTC, while the total after-tax cash flow is determined by adding after-tax savings to
FATC. To capture tax benefits for tax-paying investors, the calculation of income tax accounts for tax deductions from debt interest payments, depreciation, and tax credits for DERs [
22].
The total costs and cash flows can be used to calculate different financial indices used for optimal selection of DERs and financial feasibility analysis. MDSTool provides standard financial metrics such as NPV, lifecycle cost (LCC), benefit-cost ratio (BCR), simple payback period (SPB), internal rate of return (IRR), and annual lifecycle savings (ALCS). Hereby, MDSTool provides both pre-tax and after-tax cash flow analysis. The choice of pre-tax or after-tax analysis depends on the ownership of a microgrid project. Pre-tax financial analysis is appropriate for non-tax-paying investors such as governmental agencies, while after-tax analysis is crucial for private entities investing in microgrids because taxes and tax credits are real costs which directly affect cost and revenue streams of a commercial investment.
2.3. Microgrid Design Using MDSTool
The purpose of this study is to provide an optimal microgrid design methodology for commercial ownership based on financial feasibility analysis, which can capture all of the benefits from renewable energy incentives and tax reduction programs and evaluates their impact on the investment. For this reason, this method uses after-tax basis to compare alternative configurations.
With the objective of addressing all benefits and returns by a microgrid deployment, the metrics used in this study for financial feasibility evaluation are NPV, SPB, LCC, and BCR. NPV is computed by subtracting the present values of cash outflows from the present values of cash inflows over the lifetime of the investment, as shown in Equation (16). It is a direct measurement that determines to what extent an investment will be profitable. In the context of investment decision-making, it has been shown that NPV leads to better decisions than other indices [
29]. For this reason, NPV is selected as the primary ranking criteria to find the optimal configuration, which is the most profitable case (i.e., that with the highest NPV). In the case where there are alternative configurations which produce the same benefits and returns, the other secondary metrics will need to be evaluated. LCC is a measurement of all costs incurred during a project’s lifetime. BCR is defined as the ratio of the present value to the initial installation cost as calculated in Equation (18).
The SPB of a project is determined as the number of years it takes before the cumulative forecasted cash flow equals or exceeds the initial investment. It can be expressed as the first point in the project’s lifetime at which:
Figure 2 presents the microgrid design process using MDSTool, which is divided into two main sections: (1) Problem formulation and (2) simulation and analysis. In the first section, definitions of the design objective and the DER search space are decisive steps. The objective of a microgrid design must be clearly defined because it will decide which evaluation parameters will be used to search for the optimal configuration. In this case study, the design objective function is to maximize the economic benefits, therefore, NPV is used for the optimal DER sizing analysis. The search space in which the optimal DER capacity is located includes all of the possible capacities of the selected DERs considering associated technical and financial constraints. For example, the rated power of PV systems is typically constrained by the available installation space, especially for campus microgrids where PV modules are often installed on the rooftops of buildings [
16]. MDSTool will perform the system operation optimization to find the optimal design for further analysis.
5. Conclusions
This paper presented a design method for commercial microgrids using MDSTool, which was applied to determine the optimal design for a university campus microgrid. The optimal microgrid configuration was obtained based on financial feasibility analysis considering various renewable energy incentives. With the objective of maximizing profitability by microgrid deployment, incentives and tax benefits were found to have a strong effect on the optimal microgrid design, including the optimal mix and sizing of DERs. In particular, investment-based incentives were found to have a decisive impact because they help to decrease the high installation costs of DERs, which has been the main challenge of integrating hybrid renewable energy systems. Therefore, it is concluded that accessing all associated incentives is crucial for effective microgrid design and planning.
The results of financial feasibility analysis are beneficial to different sectors such as utility, policy makers in renewable energy development, and industry. The California market was investigated as a case study, but the proposed method can be applied to any particular area with different market conditions and incentive policies. The results indicated that investment in microgrid in a market with diversity of incentives for DER technologies will be financially attractive to commercial investors. In some countries where there are few incentive programs for microgrids, or the penetration of RESs is inadequate, this study provides a reference for the government and utility to establish incentive policies for the adoption of more renewable energy systems. Recently, many countries have set their targets for renewable energy generation to significantly reduce emissions from the power sectors. This study highlights that financial incentives for DERs play an important role in achieving renewable and sustainable energy goals.