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Renewables are increasingly penetrating power systems and impacting electricity markets supported by the stricter energy and environmental policies. To handle the variability and uncertainty of renewable generation and to provide transparent economic incentives for resources to provide flexible services, a bid-based flexible ramping products market is proposed in the CAISO and MISO markets. To investigate the impact of these new products on the market equilibrium, a multi-period Nash-Cournot equilibrium model, formulated as a bi-level optimization problem, was proposed, and the Guass-Seidel iterative method was used to obtain the equilibrium. Moreover, a general framework of co-optimization of energy and flexible ramping products was established, and different types of generators, including thermal units, hydro units, renewable units and energy storage systems, are simultaneously considered to reflect their strategic interactions. Two cases with dominant solar power and wind power, respectively, have been implemented and are compared to demonstrate the impact of flexible ramping products on market prices, unit commitment and renewable integration. Additional energy storage systems are also included in the above case for further analysis. Simulation results show that when introducing the new products: the energy prices will increase slightly under normal conditions, more highly variable renewables can be integrated, the unit commitment will be changed, and more generators should be on line to provide flexible services, etc. [ABSTRACT FROM AUTHOR]

Self-aligned multiple patterning, due to its low overlay error, has emerged as the leading option for 1-D gridded back-end-of-line (BEOL) in sub-14-nm nodes. To form actual routing patterns from a uniform “sea of wires,” cut masks are needed for line-end cutting or realization of space between routing segments. The line-end cutting results in nonfunctional (i.e., dummy fill) patterns that change wire capacitance, and hence design timing and power. Therefore, to remove such dummy fill patterns, extra 2-D block masks are used. However, 2-D block masks cannot remove arbitrary dummy fill patterns, due to design rule constraints on the block mask shapes. In this paper, we address the timing-aware optimization of 2-D block mask layouts under various sets of mask rules that are derived from mask patterning technology options (e.g., 193i and 193d) for foundry 7-/5-nm (N7/N5) BEOL. Our central contribution is a mixed integer linear programming (MILP) optimization that minimizes timing impact due to dummy metal segments while satisfying block mask rules and metal density constraints. We also propose a distributed optimization flow to improve the scalability. With our optimizer, we recover up to 84% of the worst negative slack impact from dummy segments, with up to 64% dummy removal rate. We further extend our MILP to a co-optimization of cut and block masks. This paper gives new insights into fundamental limits of benefit from emerging cut and block mask technology options. [ABSTRACT FROM AUTHOR]

This paper presents an integrated numerical framework to co-optimize EOR and CO 2 storage performance under uncertainty in the Farnsworth Unit (FWU) oil field in Ochiltree County, Texas. The framework includes a field-scale compositional reservoir multiphase flow model, an uncertainty quantification model and a neural network optimization process. The reservoir flow model has been constructed based on the field geophysical, geological, and engineering data. Equation of state parameters were tuned to achieve field measured fluid properties and subsequently used to predict the minimum miscible pressure (MMP). A history match of primary and secondary recovery processes was conducted to estimate the reservoir and multiphase flow parameters as the base case for analyzing the effect of recycling produced gas, infill drilling and water alternating gas (WAG) cycles on oil recovery and CO 2 storage. A multi-objective optimization model was defined for maximizing both oil recovery and CO 2 storage. The uncertainty quantification model comprising the Latin Hypercube sampling, Monte Carlo simulation, and sensitivity analysis, was used to study the effects of uncertain variables on the defined objective functions. Uncertain variables include bottom hole injection pressure, WAG cycle, injection and production group rates, and gas-oil ratio. The most significant variables were chosen as control variables to be used for the optimization process. A neural network optimization algorithm was utilized to optimize the objective function both with and without geological uncertainty. The vertical permeability anisotropy (Kv/Kh) was selected as one of the uncertain parameters in the optimization process. The simulation results were compared to a scenario baseline case that predicted CO 2 storage of 74%. The results showed an improved approach for optimizing oil recovery and CO 2 storage in the FWU. The optimization model predicted that about 94% of CO 2 would be stored and most importantly, that this increased storage could result in about 25% of incremental oil recovery. The sensitivity analysis reduced the number of control variables to decrease computational time. A risk aversion factor was used to represent results at various confidence levels to assist management in the decision-making process. The defined objective functions were shown to be a robust approach to co-optimize oil recovery and CO 2 storage. [ABSTRACT FROM AUTHOR]

This paper presents an integrated numerical framework to co-optimize EOR and CO 2 storage performance in the Farnsworth field unit (FWU), Ochiltree County, Texas. The framework includes a field-scale compositional reservoir flow model, an uncertainty quantification model and a neural network optimization process. The reservoir flow model has been constructed based on the field geophysical, geological, and engineering data. A laboratory fluid analysis was tuned to an equation of state and subsequently used to predict the thermodynamic minimum miscible pressure (MMP). A history match of primary and secondary recovery processes was conducted to estimate the reservoir and multiphase flow parameters as the baseline case for analyzing the effect of recycling produced gas, infill drilling and water alternating gas (WAG) cycles on oil recovery and CO 2 storage. A multi-objective optimization model was defined for maximizing both oil recovery and CO 2 storage. The uncertainty quantification model comprising the Latin Hypercube sampling, Monte Carlo simulation, and sensitivity analysis, was used to study the effects of uncertain variables on the defined objective functions. Uncertain variables such as bottom hole injection pressure, WAG cycle, injection and production group rates, and gas-oil ratio among others were selected. The most significant variables were selected as control variables to be used for the optimization process. A neural network optimization algorithm was utilized to optimize the objective function both with and without geological uncertainty. The vertical permeability anisotropy (Kv/Kh) was selected as one of the uncertain parameters in the optimization process. The simulation results were compared to a scenario baseline case that predicted CO 2 storage of 74%. The results showed an improved approach for optimizing oil recovery and CO 2 storage in the FWU. The optimization process predicted more than 94% of CO 2 storage and most importantly about 28% of incremental oil recovery. The sensitivity analysis reduced the number of control variables to decrease computational time. A risk aversion factor was used to represent results at various confidence levels to assist management in the decision-making process. The defined objective functions were proved to be a robust approach to co-optimize oil recovery and CO 2 storage. The Farnsworth CO 2 project will serve as a benchmark for future CO 2 –EOR or CCUS projects in the Anadarko basin or geologically similar basins throughout the world. [ABSTRACT FROM AUTHOR]

This paper takes a close look at the important commonalities and subtle differences between the well-established field of supervised learning and the much younger one of cooptimization. It explains the relationships between the problems, algorithms and views on cost and performance of the two fields, all throughout providing a two-way dictionary for the respective terminologies used to describe these concepts. The intent is to facilitate advancement of both fields through transfer and cross-pollination of ideas, techniques and results. As a proof of concept, a theoretical study is presented on the connection between existence / lack of free lunch in the two fields, showcasing a few ideas for improving computational complexity of certain supervised learning approaches. [ABSTRACT FROM AUTHOR]

The significant growth of gas-fired power plants and emerging power-to-gas (PtG) technology has intensified the interdependency between electricity and natural gas systems. This paper proposes a robust co-optimization scheduling model to study the coordinated optimal operation of the two energy systems. The proposed model minimizes the total costs of the two systems, while considering power system key uncertainties and natural gas system dynamics. Because of the limitation on exchanging private data and the challenge in managing complex models, the proposed co-optimization model is tackled via alternating direction method of multipliers (ADMM) by iteratively solving a power system subproblem and a gas system subproblem. The power system subproblem is solved by column-and-constraint generation (C&CG) and outer approximation (OA), and the nonlinear gas system subproblem is solved by converting into a mixed-integer linear programming model. To overcome nonconvexity of the original problem with binary variables, a tailored ADMM with a relax-round-polish process is developed to obtain high-quality solutions. Numerical case studies illustrate the effectiveness of the proposed model for optimally coordinating electricity and natural gas systems with uncertainties. [ABSTRACT FROM PUBLISHER]

The three emerging technologies (renewable energy, energy storage and demand response) play important roles in the co-optimized electricity and ancillary service (EAS) markets where electricity and ancillary service are simultaneously dispatched. While promising, we notice that most literature focuses on either technology integration or operation in the EAS markets. In this research, we develop a three-stage data-driven multi-criteria analytical framework to enable the optimal integration of emerging technologies and operation decisions in an EAS market context under various conditions. We propose multiple performance metrics to evaluate the EAS markets and use a Latin hypercube sampling approach to generate training data for these metrics based on a mixed integer quadratic programming model. Various data-driven models are developed for the performance metrics using the training data and two multi-criteria decision models based on the data-driven models are developed to select optimal technologies based on various criteria. To demonstrate the effectiveness of the proposed framework, we study a revised IEEE 118-bus system. It is demonstrated that our proposed approach can: 1) characterize the relations between each performance metric and technology parameters, 2) determine the significant impact technologies for each performance metric, and 3) recommend optimal emerging technologies integration for market/system operators. [ABSTRACT FROM AUTHOR]

This paper presents an optimization methodology for CO2 enhanced oil recovery in partially depleted reservoirs. A field-scale compositional reservoir flow model was developed for assessing the performance history of an active CO2 flood and for optimizing both oil production and CO2 storage in the Farnsworth Unit (FWU), Ochiltree County, Texas. A geological framework model constructed from geophysical, geological, and engineering data acquired from the FWU was the basis for all reservoir simulations and the optimization method. An equation of state was calibrated with laboratory fluid analyses and subsequently used to predict the thermodynamic minimum miscible pressure (MMP). Initial history calibrations of primary, secondary and tertiary recovery were conducted as the basis for the study. After a good match was achieved, an optimization approach consisting of a proxy or surrogate model was constructed with a polynomial response surface method (PRSM). The PRSM utilized an objective function that maximized both oil recovery and CO2 storage. Experimental design was used to link uncertain parameters to the objective function. Control variables considered in this study included: water alternating gas cycle and ratio, production rates and bottom-hole pressure of injectors and producers. Other key parameters considered in the modeling process were CO2 purchase, gas recycle and addition of infill wells and/or patterns. The PRSM proxy model was 'trained' or calibrated with a series of training simulations. This involved an iterative process until the surrogate model reached a specific validation criterion. A sensitivity analysis was first conducted to ascertain which of these control variables to retain in the surrogate model. A genetic algorithm with a mixed-integer capability optimization approach was employed to determine the optimum developmental strategy to maximize both oil recovery and CO2 storage. The proxy model reduced the computational cost significantly. The validation criteria of the reduced order model ensured accuracy in the dynamic modeling results. The prediction outcome suggested robustness and reliability of the genetic algorithm for optimizing both oil recovery and CO2 storage. The reservoir modeling approach used in this study illustrates an improved approach to optimizing oil production and CO2 storage within partially depleted oil reservoirs such as FWU. This study may serve as a benchmark for potential CO2-EOR projects in the Anadarko basin and/or geologically similar basins throughout the world. © 2016 Society of Chemical Industry and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

To achieve the most efficient restoration of hybrid AC/DC distribution system, this paper proposes an outage management through co-optimizing service restoration with repair crew (RC) and mobile energy storage system (MESS) dispatch. Firstly, this paper proposes a hybrid AC/DC distribution system restoration (DSR) model considering network reconfiguration, distributed generation (DG) islanded operation and voltage source converter (VSC) based DSR, which means adjusting VSC control mode to realize uninterrupted power supply of loads. In the DSR model, a restoration coordination model that can coordinate network reconfiguration and the control modes of VSCs, DGs and MESS discharge stations is developed to ensure the radial configuration of the system. Moreover, to accurately calculate the power flow of the system during a failure of VSCs, a new power flow model considering VSC's operation status is proposed in the DSR model. Then, a bi-level co-optimization method is developed based on the DSR model and improved dispatch models of RC and MESS. The upper level co-optimizes hybrid AC/DC distribution system restoration with RC and MESS dispatch to maximize the restored loads. The lower level further optimizes the power outputs of VSCs and DGs to minimize the voltage deviation of the restored loads. Finally, simulations studies are performed to verify the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Due to the variable nature of wind resources, the increasing penetration level of wind power will have a significant impact on the operation and planning of the electric power system. Energy storage systems are considered an effective way to compensate for the variability of wind generation. This paper presents a detailed production cost simulation model to evaluate the economic value of compressed air energy storage (CAES) in systems with large-scale wind power generation. The co-optimization of energy and ancillary services markets is implemented in order to analyze the impacts of CAES, not only on energy supply, but also on system operating reserves. Both hourly and 5-minute simulations are considered to capture the economic performance of CAES in the day-ahead (DA) and real-time (RT) markets. The generalized network flow formulation is used to model the characteristics of CAES in detail. The proposed model is applied on a modified IEEE 24-bus reliability test system. The numerical example shows that besides the economic benefits gained through energy arbitrage in the DA market, CAES can also generate significant profits by providing reserves, compensating for wind forecast errors and intra-hour fluctuation, and participating in the RT market.

This paper proposes a system model for optimal dispatch of the energy and reserve capacity considering uncertain load demand and unsteady power generation. This implicates uncertainty in managing the power demand along with the consideration of utility, user and environmental objectives. The model takes into consideration a day-ahead electricity market that involves the varying power demand bids and generates a required amount of energy in addition with reserve capacity. The lost opportunity cost is also considered and incorporated within the context of expected load not served. Then, the effects of combined and separate dispatching the energy and reserve are investigated. The nonlinear cost curves have been addressed by optimizing the objective function using robust optimization technique. Finally, various cases in accordance with underlying parameters have been considered in order to conduct and evaluate numerical results. Simulation results show the effectiveness of proposed scheduling model in terms of reduced cost and system stability.

After disasters, enhancing the resilience of power systems and restoring power systems rapidly can effectively reduce the economy damage and bad social impacts. Reasonable post-disaster restoration strategies are the most critical part of power system restoration work. This paper co-optimizes post-disaster damage repair and power system operation together to formulate the optimal repair route, the unit output and transmission switching plan. The power outage loss will be minimized, with possible small expense of damage repair and power system operation cost. The co-optimization model is formulated as a mixed integer second order cone program (MISOCP), while the AC-power-flow model, the complex power system restoration constraints and the changing processes of component available states are synthetically considered to make the model more realistic. Lagrange relaxation (LR) decomposes the model into the damage repair routing sub problem and the power system operation sub problem, which can be solved iteratively. An acceleration strategy is used to improve the solving efficiency. The proposed model and algorithm are validated by the IEEE 57-bus test system and the results indicate that the proposed model can realize the enhancement of resilience and the economic restoration of post-disaster power systems.

Underground hydrogen storage is a suitable way to create a balance between energy production and consumption. However, if it is able to incorporate a solution such as Carbon Capture and Sequestration, it has brought double benefit to the environment. Hydrogen has a good potential for energy production and transfer, and its behavior in the porous media has differences compared to other gases. In order to achieve the highest recovery factor of hydrogen and carbon dioxide storage and high utility of the project, the operational parameters of underground hydrogen storage and carbon dioxide storage should be carefully determined. The optimization of these parameters is challenging, complex, and time-consuming. Artificial intelligence methods have been developed using optimization methods in this regard. Simulation and investigation of underground hydrogen storage in depleted oil reservoirs were conducted in this study. Using machine learning methods, a proxy model for the UHS process was constructed, and then the operating parameters of UHS are optimized with the help of NSGA-II algorithm under the three objective functions of Net Present Value, hydrogen recovery factor, and CCS. Multi-layer neural networks with different architectures were trained and evaluated on the database. Research results are promising, and the approach is inspiring. It was found that the selected proxy model was able to accurately predict all three objective functions, with a R-squared of 0.9988 and a relative squared error of 0.00048. When an accurate model for NPV is not available, two-objective optimization is more efficient, but if an accurate model is available, adding NPV as a third objective function is necessary to achieve economic solutions. In both optimization perspectives, two Pareto fronts including 500 optimal solutions were determined separately. The considered NPV model correlates better with the objective function of carbon dioxide storage, while it is less consistent with the objective function of hydrogen recovery. [ABSTRACT FROM AUTHOR]

The Water-Energy-Food (WEF) Nexus approach has an opportunity to create sustainable business solutions, especially in the agricultural sector. This report provides a clear understanding of challenges for the most promising and scalable solutions to the interconnected water, energy and food/feed/fiber problems, allowing combined co-optimization. Searching solutions for nexus challenges, a model based assessment of WEF nexus at global scale and a theoretical perspective of integrating economic and ecological objectives are presented. This study explores the opportunities for the global community to develop robust partnerships and cross sector collaboration to foster water security and increase resource use efficiency, particularly in agriculture and food industry, as part of corporate environmental and financial performance goals.

The recognition of transmission's interaction with other resources has motivated the development of co-optimization methods to optimize transmission investment while simultaneously considering tradeoffs with investments in electricity supply, demand, and storage resources. For a given set of constraints, co-optimized planning models provide solutions that have lower costs than solutions obtained from decoupled optimization (transmission-only, generation-only, or iterations between them). This paper describes co-optimization and provides an overview of approaches to co-optimizing transmission options, supply-side resources, demand-side resources, and natural gas pipelines. In particular, the paper provides an up-to-date assessment of the present and potential capabilities of existing co-optimization tools, and it discusses needs and challenges for developing advanced co-optimization models. [ABSTRACT FROM AUTHOR]

This article put forward a theoretical system, which is used to analyze co-optimization of CO2sequestration and enhanced oil recovery by using the methods or theories including the design of experiments, numerical simulation, net present value, and the response surface methods. A CO2flooding operation in an immiscible water-alternating-gas process of Shanbei extra-low permeability reservoir was estimated preliminarily by using the methods proposed in this article. The result shows that optimized values obtained from predicted values were in good agreement with the values obtained from the simulation model and it is possible to optimize a coupled CO2sequestration and enhanced oil recovery project. [ABSTRACT FROM PUBLISHER]

This paper proposes a two-stage distributionally robust co-optimization model for the microgrid offering problem. This model addresses the market participation of the microgrid including offering, market-clearing and reserve activation stages and captures the information availability from the market-side and the local distributed energy resource (DER) assets during the different stages. The DER assets are modeled through state-space models that capture the joint energy and contingency reserves provision capability for distributed generators, flexible building loads and battery storage systems. Moreover, uncertainties pertaining to market-clearing process, renewable generation, solar irradiation and ambient temperature are modeled in an ambiguity set that comprises statistical characterizations. Distributionally robust optimization (DRO) is employed to generate robust, but less conservative solutions. Linear decision rules and the second-order conic reformulations are employed to ensure the computational tractability of the DRO problem. The suitability of the proposed model is demonstrated for a synthetic multi-phase microgrid with spot market data from Singapore. • We apply the distributionally robust optimization (DRO) method to solve microgrid offering problem. • Linear decision rules are applied to solve the DRO problem formulation. • We have covered a broad spectrum of uncertainty modeling. [ABSTRACT FROM AUTHOR]

Driven by the recent trends towards a smart power system configuration, there has been a greater focus within the literature on the integration between supply and demand-side resources. The contribution of this research is multifold since it provides a timely and required study, offering valuable insights into how the integration between Demand-Side Management (DSM) resources with clean energy supply options might affect the long-term power planning strategies in high-renewable electricity systems. The innovative aspects of this study are strongly related to a framework proposal for assessing the co-benefits between energy efficiency and demand-response on renewable-based energy systems from a long-term perspective using a co-optimization modelling approach. Overall, the results indicate that a clear benefit of implementing DSM strategies is the percentage reduction of the new installed capacity (−1.0% to −20.6%), CO 2 emissions (−2.4% to −11.8%), and total system costs (−0.5% to −15.8%) for all scenarios compared to Business-As-Usual scenario. The evidence from this study suggests that investments in Energy Efficiency (EE) seem more economically valuable than investing only in Demand-Response (DR) strategies. However, integrating EE and DR would empower the overall power system benefits. Strong evidence for a higher potential to delay investments was found for all scenarios for the first ten years of the planning period. Our findings might provide valuable insights to both governments and policy-makers by delivering supportive information in scaling up EE investments. The methodology proposed offers essential contributions to the scientific community and would benefit energy systems research beyond the cases addressed in this paper. • Integrated assessment of demand-side management for renewable-based energy systems is performed. • The methodology accounts for long-term reinvestments in energy efficiency. • A co-optimization modelling approach is used for the scenario analysis. • Combining energy efficiency and demand-response investments empower the economic benefits. • Demand-side strategies seem to decrease the natural gas capacity for all scenarios. [ABSTRACT FROM AUTHOR]

Natural gas is an important fuel source in the power industry. Electricity and natural gas are both energy that can be directly consumed. To improve the overall efficiency of the energy infrastructure, it is imperative that the expansion of gas power plants, electricity transmission lines and gas pipelines can be co-planned. The co-planning process is modeled as a mixed integer nonlinear programming problem to handle conflicting objectives simultaneously. We propose a novel model to identify the optimal co-expansion plan in terms of social welfare. To evaluate the robustness of plans under different scenarios, the flexibility criterion is used to identify each plan’s adaptation cost to uncertainties, such as demand growth, fuel cost and financial constraints, etc. We developed a systematic and comprehensive planning model to understand, develop and optimize energy grids in order to reach higher social welfare, and is therefore of great importance in terms of supporting and guiding investment decisions for the power and gas industry. Meanwhile, we use the sequential importance sampling (SIS) to perform scenario reduction for achieving a higher computational efficiency. A comprehensive case study on the integrated IEEE 14-bus and a test gas system is conducted to validate our approach. [ABSTRACT FROM PUBLISHER]

In this paper, we proposed a low noise amplifier (LNA) for ultra wideband (UWB) applications using TSMC 0.18µm CMOS technology. LNA and high frequency H-shaped antenna have been utilized to solve problems of each block, such as the necessity of being matched with 50 Ohm impedance, so we have one more degree of freedom and a more complex system, although our choosing ability is increased. The targeted is Ultra Wide Band where this architecture and a Co-Designed version, with UWB-LNA architecture offer one of the best compromises between gain and noise figure. For having the best trade-offs between these two cases in UWB-LNA, both Multi-Objective Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used successfully. Symmetric nature is obtained both in terms of S-parameter as well as flatness over a wide bandwidth for gain and noise figure. To have better Co-Design; in this work another optimization on antenna parameters performed with ADS Software. In co-optimization with the aid of two techniques; LNA parameters improvement utilizing evolutionary algorithms and antenna parameters enhancement in co-design output S-parameters had more promising results. Results show gain-flatness is being enhanced in desired frequency band. Noise factor is improved by more1.5 dB over the whole bandwidth [3.1GHz-8.1GHz]and supply voltage of 1.8V. [ABSTRACT FROM AUTHOR]

As a clean fuel source, natural gas plays an important role in achieving a low-carbon economy in the power industry. Owing to the uncertainties introduced by increasing utilization of natural gas in electric power system, gas system and electricity system should be planned in an integrated manner. When considering these two systems simultaneously, there are many emerging difficulties, e.g., increased system complexity and risk, market timeline mismatch, overall system reliability evaluation, etc. In this paper, a novel expansion co-planning (ECP) framework is proposed to address the above challenges. In our approach, the planning process is modeled as a mixed integer nonlinear optimization problem. The best augmentation option is a plan with the highest cost/benefit ratio. Benefits of expansion planning considered are reductions in operation cost, carbon emission cost, and unreliability cost. By identifying several scenarios based on statistical analysis and expert knowledge, decision analysis is used to tackle market uncertainties. The operational and economic interdependency of both systems are well analyzed. Case studies on a three-bus gas and two-bus power system, plus the Victorian integrated gas and electricity system in Australia are presented to validate the performance of the proposed framework. [ABSTRACT FROM PUBLISHER]

Traditional black-box optimization searches a set of potential solutions for those optimizing the value of a function whose analytical or algebraic form is unknown or inexistent, but whose value can be queried for any input. Co-optimization is a generalization of this setting, in which fully evaluating a potential solution may require querying some function more than once, typically a very large number of times. When that's the case, co-optimization poses unique difficulties to designing and assessing algorithms. A generally-applicable approach is to judge co-optimization algorithm performance via an aggregate over all possible functions in the problem domain. We establish formal definitions of such aggregate performance and then investigate the following questions concerning algorithm design: 1) are some algorithms strictly better than others? i.e. is there "free lunch"? 2) do optimal algorithms exist? and 3) if so, are they practical? We formally define free lunch and aggregate optimality of co-optimization algorithms and derive generic conditions for their existence. We review and explain prior (no) free lunch results from the perspective of these conditions; we also show how this framework can be used to bridge several fields of research, by allowing formalization of their various problems and views on performance. We then apply and extend the generic results in a context involving a particular type of co-optimization called worst-case optimization. In this context we show that there exist algorithms that are aggregately-optimal for any budget (allowed number of function calls) and any starting point (set of previously uncovered function call outcomes), and also non-trivially strictly optimal for many budgets and starting points; moreover, we formalize the operation of such optimal algorithms and show that for certain domains, budgets and starting points this operation is equivalent to a simple procedure with tractable implementation--a first-of-its-kind result for co-optimization. [ABSTRACT FROM AUTHOR]

FERC Order 755 requires each RTO/ISO to use market-based mechanisms to select and compensate frequency regulation resources based on a two-part payment methodology, i.e., a capacity payment to keep the capacity in reserve and a performance payment to reflect the amount of work each resource performs in real-time in response to the system operator's dispatch signal. This paper discusses how MISO enhanced its market rules to implement a market-based regulation performance payment. The market enhancement includes four key areas: quantifying the movement of regulation, modifications to the market clearing processes, performance accuracy measurement, and performance-based two-part regulation compensation. [ABSTRACT FROM PUBLISHER]

When juices rich in fruit pulp are filtered, the polysaccharides present in the cell walls, such as pectin, cellulose, hemicellulose and lignin, are responsible for the formation of a layer called clogging. In such cases, these polysaccharides are hydrolyzed by enzymes to improve filtering. The objective of this study was to evaluate the effect of enzyme concentration and incubation time on clarification variables of mango bocado juice, which was obtained by vacuum filtration equipment 0.5 cmHg using as medium Whatman filter paper No. 1. The responses studied were filterability, total soluble solids (TSS), turbidity, absorbance and performance of the filter (PF). For obtaining the trial treatments, a response surface design was applied to two factors: enzyme concentration and time of incubation, with five levels each and one repetition of the assay for a total of 24 experimental units. The relevant results were: that increasing the enzyme concentration from 100 - 1000 mg*L-1, filterability, TSS and performance in filtered mango bocado juice was increased. The enzyme - time interaction (IET) caused a highly significant effect on the study variables and showed that as the EIT increases filterability and increased SST in the permeate, opposite behavior was visualized in absorbance, turbidity and performance, which were inversely proportional to the IET. [ABSTRACT FROM AUTHOR]

• Propose a new emergency scheduling framework for distribution power systems under the cyber-physical-social system perspective. • Build a novel co-scheduling model with the goal of minimizing time-consuming, cost, and shedding load as a mixed-integer non-linear optimization model • Formulate this problem as an advanced multiple salesmen traveling problem with a routing stage and a scheduling stage. • Analyze the routing and scheduling results and compare the co-optimization model with the individual optimization process. With the frequently happened aging electrical grid and extreme weather events, outage management and service restoration face the challenges of flexible scheduling multiple resources for different kinds of objectives. Previous research mainly focuses on the single restoration objective or single emergency resource management. This paper proposes a new emergency scheduling framework under the cyber-physical-social system (CPSS) perspective, which includes the scheduling of multiple components from CPSS, i.e., crew, mobile distributed generators, and resources. Under the framework, a novel co-scheduling model with the goal of minimizing time-consuming, cost, and shedding load are also built as a mixed-integer non-linear optimization model. Then we formulate this problem as advanced multiple salesmen traveling problem with a routing stage and a scheduling stage, and design a solution algorithm based on the genetic algorithm to solve this problem. In the solving algorithm, the optimal routing solutions are got at first, then the emergency scheduling solutions are optimized in the second stage based on the routing results. Finally, a numerical case study on the IEEE 123-bus network shows the effectiveness of the co-optimization process, which can provide a routing solution for each depot, and the corresponding optimization objective results will increase 25.75% and 73.92% when compared with time-consuming optimization and load-shedding optimization. [ABSTRACT FROM AUTHOR]

In modern power system planning and operation, the relevance of distributed power generation units and demand-side flexibility options grows. As a consequence, smart planning and operation routines become necessary also in the distribution grid. We therefore develop a co-optimization framework to consider transmission and distribution system constraints and their interactions together. This enables bidirectional flows between both systems, supporting a consistent and cost-efficient overall energy system. The key feature of this paper is an automated coupling of two independently and modularly defined systems. It allows a bottom-up modelling approach of energy demands, conversion and storage technologies from both system levels. However, this approach increases the model size significantly. Thus, timeseries aggregation methods are applied to reduce the computational complexity. The results for the optimal planning of the coupled German energy system show that the electrification of heat and mobility sectors in the distribution grid is not only beneficial to achieve carbon neutrality in 2050, but also to particularly make use of additional flexibility potentials. Our results underline a significant positive correlation between a smart operation of heat pumps and charging stations with the maximum integrable capacities of low-cost rooftop photovoltaics in the distribution system. • Open-source framework to couple transmission system and active distribution grids. • Co-optimization of both system levels enables a holistic model with a global optimum. • Case study: German energy system with the carbon-neutrality target by 2050. • Electrification of residential heat and mobility sector to unlock flexibilities. • Gained flexibilities promote 20% higher PV capacities and reduce system costs by 7%. [ABSTRACT FROM AUTHOR]

Work using the concept of a co-optimization-based metrology hybridization is presented. Hybrid cooptimization involves the combination of data from two or more metrology tools such that the output of each tool is improved by the output of the other tool. Here, the image analysis parameters from a critical dimension scanning electron microscope (CD-SEM) are modulated by the profile information from optical critical dimension (OCD, or scatterometry), while the OCD-extracted profile is concurrently optimized through addition of the CD-SEM CD results. The test vehicle utilized is the 14-nm technology node-based FinFET high-k/interfacial layer (HK/IL) structure. When compared with the nonhybrid approach, the correlation to reference measurements of the HK layer thickness measurement using hybrid co-optimization resulted in an improvement in relative accuracy of about 40% and in R2 from 0.81 to 0.91. The measurement of the IL thickness also shows an improvement with hybrid co-optimization: better matching to the expected conditions as well as data that contain less noise. [ABSTRACT FROM AUTHOR]

We develop a stochastic dynamic programming model that co-optimizes the use of energy storage for multiple applications, such as energy, capacity, and backup services, while accounting for market and system uncertainty. Using the example of a battery that has been installed in a home as a distributed storage device, we demonstrate the ability of the model to co-optimize services that 'compete' for the capacity of the battery. We also show that these multiple uses of a battery can provide substantive value. [ABSTRACT FROM AUTHOR]