Microgrid Operations Planning Based on Improving the Flying Sparrow Search Algorithm

Microgrid operations planning is crucial for emerging energy microgrids to enhance the share of clean energy power generation and ensure a safe symmetry power grid among distributed natural power sources and stable functioning of the entire power system. This paper suggests a new improved version (namely, ESSA) of the sparrow search algorithm (SSA) based on an elite reverse learning strategy and firefly algorithm (FA) mutation strategy for the power microgrid optimal operations planning. Scheduling cycles of the microgrid with a distributed power source’s optimal output and total operation cost is modeled based on variables, e.g., environmental costs, electricity interaction, investment depreciation, and maintenance system, to establish grid multi-objective economic optimization. Compared with other literature methods, such as Genetic algorithm (GA), Particle swarm optimization (PSO), Firefly algorithm (FA), Bat algorithm (BA), Grey wolf optimization (GWO), and SSA show that the proposed plan offers higher performance and feasibility in solving microgrid operations planning issues.

Integrated sustainability policy assessment – an agent-based ecological-economic model

The paper proposes an agent-based evolutionary ecological-economic model that captures the link between the economy and the ecosystem in a more inclusive way than standard economic optimization models do. We argue that an evolutionary approach is required to understand the integrated dynamics of both systems, i.e. micro–macro feedbacks. In the paper, we illustrate that claim by analyzing the non-triviality of finding a sustainability policy mix as a use case for such a coupled system. The model has three characteristics distinguishing it from traditional environmental and resource economic models: (1) it implements a multi-dimensional link between the economic and the ecological system, considering side effects of production, and thus combines the analyses of environmental and resource economics; (2) following literature from biology, it uses a discrete time approach for the biological resource allowing for the whole range of stability regimes instead of artificially stabilizing the system, and (3) it links this resource system to an evolving, agent-based economy (on the basis of a Nelson-Winter model) with bounded rational decision makers instead of the standard optimization model. The policy case illustrates the relevance of the proposed integrated assessment as it delivers some surprising results on the effects of combined and consecutively introduced policies that would go unnoticed in standard models.

Enabling Production of Algal Biofuels by Techno-Economic Optimization of Co-Product Suites

Recent techno-economic analysis (TEA) has underscored that for algal biofuels to be cost competitive with petroleum fuels, co-products are necessary to offset the cost of fuel production. The co-product suite must scale with fuel production while also maximizing value from the non-fuel precursor components. The co-product suite also depends on algal biomass composition, which is highly dynamic and depends on environmental conditions during cultivation. Intentional shifts in composition during cultivation are often associated with reduced biomass productivity, which can increase feedstock production costs for the algae-based biorefinery. The optimal algae-based biorefinery configuration is thus a function of many factors. We have found that comprehensive TEA, which requires the construction of process models with detailed mass and energy balances, along with a complete accounting of capital and operating expenditures for a commercial-scale production facility, provides invaluable insight into the viability of a proposed biorefinery configuration. This insight is reflected in improved viability for one biorefining approach that we have developed over the last 10 years, namely, the Combined Algal Processing (CAP) approach. This approach fractionates algal biomass into carbohydrate-, lipid-, and protein-rich fractions, and tailors upgrading chemistry to the composition of each fraction. In particular, transitioning from valorization of only the lipids to a co-product suite from multiple components of high-carbohydrate algal biomass can reduce the minimum fuel selling price (MFSP) from more than $8/gallon of gasoline equivalent (GGE) to $2.50/GGE. This paper summarizes that progress and discusses several surprising implications in this optimization approach.

Extended-Time Conductivity Testing of Proppants Used for Multi-Stage Horizontal Completions

The American Petroleum Institute Recommended Practice 19-D (2018) is the current industry standard for conductivity testing of proppants used in hydraulic fracturing. Similar to previous standards from both the API and ISO, it continues the practice of measuring a "reference" long-term conductivity after 50-hours of time at a given stress. The fracture design engineer is then left to estimate a damage factor to apply over the life of the well completion based on correlations or experience. This study takes four standard proppants used for multi-stage horizontal well completions in North America and presents test data over 250-days of "extended-time" at 7,500 psi of effective stress. The API RP 19-D procedure was followed for all testing, but extended for 250-days duration for the four proppant types: 40/70 mesh mono-crystalline "White" sand, 40/70 mesh multi-crystalline "Brown" sand, 100 mesh "Brown" sand, and 40/70 mesh Light Weight Ceramic (LWC). The 7,500 psi stress condition was chosen to replicate initial stress conditions for a 10,000 feet deep well with a 0.75 psi/ft fracture gradient - typical of unconventional resource plays such as the Bakken formation of North Dakota or the Delaware Basin in west Texas. Results presented provide a measure of the amount of damage occurring in the proppant pack due to time at stress. To the authors’ knowledge, there has never been any extended-time conductivity data published for multiple proppant types over the timeframe completed in this study - despite the obvious need for this understanding to optimize the stimulation design over the full life of the well. Results for the four proppant types are presented as conductivity curves as a function of time for the 250-days of testing. Pack degradation is shown to follow a semi-log decline. Late time continued degradation for all materials is extrapolated over the life of a typical well (40 years), and compared to extended-time particle size distribution and crush data to explain the results observed. Extended-time data such as this 250-day study have never been published on proppants such as these despite the fact that fracture conductivity has a major impact on the productive life of a well and the ultimate recovery of hydrocarbons from the formation. The data presented should be of great interest to any engineer involved with completion designs, or reservoir engineers assessing the productive life and ultimate recovery in the formation since economic optimization is primarily driven by the interplay of fracture length/area with extended-time in-situ fracture conductivity.

Multi-Objective Techno-Economic Optimization of Design Parameters for Residential Buildings in Different Climate Zones

The comprehensive approach for a building envelope design involves building performance simulations, which are time-consuming and require knowledge of complicated processes. In addition, climate variation makes the selection of these parameters more complex. The paper aims to establish guidelines for determining a single-family household’s unique optimal passive design in various climate zones worldwide. For this purpose, a bi-objective optimization is performed for twenty-four locations in twenty climates by coupling TRNSYS and a non-dominated sorting genetic algorithm (NSGA-III) using the Python program. The optimization process generates Pareto fronts of thermal load and investment cost to identify the optimum design options for the insulation level of the envelope, window aperture for passive cooling, window-to-wall ratio (WWR), shading fraction, radiation-based shading control, and building orientation. The goal is to find a feasible trade-off between thermal energy demand and the cost of thermal insulation. This is achieved using multi-criteria decision making (MCDM) through criteria importance using intercriteria correlation (CRITIC) and the technique for order preference by similarity to ideal solution (TOPSIS). The results demonstrate that an optimal envelope design remarkably improves the thermal load compared to the base case of previous envelope design practices. However, the weather conditions strongly influence the design parameters. The research findings set a benchmark for energy-efficient household envelopes in the investigated climates. The optimal solution sets also provide a criterion for selecting the ranges of envelope design parameters according to the space heating and cooling demands of the climate zone.