Smart microgrids (SMGs) represent an innovative and adaptive approach to energy distribution, integrating advanced technologies and intelligent control systems to enhance energy efficiency and reliability. In these SM...
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We introduce Virtual integer-Real Arithmetic Substitution (Viras), a quantifier elimination procedure for deciding quantified linearmixedinteger-real arithmetic problems. Viras combines the framework of virtual subs...
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The increasing numbers of distributed energy resources (DER) incur new challenges for the energy supply due to the volatilities and uncertainties of renewable energies. To utilize the benefits of DER, a combination wi...
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Clusterwise Regression (CLR) methods that jointly optimize clustering and regression tasks are useful for partitioning data into disjoint subsets with distinct regression trends. Due to the inherent difficulty in simu...
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ISBN:
(纸本)9783031332708;9783031332715
Clusterwise Regression (CLR) methods that jointly optimize clustering and regression tasks are useful for partitioning data into disjoint subsets with distinct regression trends. Due to the inherent difficulty in simultaneously optimizing clustering and regression objectives, it is not surprising that existing optimal CLR approaches do not scale beyond 100 s of data points. In an effort to provide more scalable and optimal CLR methods, we propose a novel formulation of the problem that takes inspiration from e-tubes in Support Vector Regression (SVR). The advantage of this novel formulation, which aims to assign data points to clusters in order to minimize the largest e-tube that encapsulates the regressed data, is that it admits an optimal MILP formulation. Furthermore, given that each constraint in our formulation corresponds to a single data point, we propose an efficient row generation solution that can optimally converge for the full dataset while only requiring optimization over a subset of the data. Our results on a variety of synthetic and benchmark real datasets show that our Clusterwise Regression MILP formulation provides near-optimal solutions up to 100,000 data points and the smallest data-encapsulating e-tubes among CLR alternatives.
Isolated power systems with high shares of renewables can require additional inertia as a complementary resource to assure the system operation in a dynamic safe region. This paper presents a methodology for the day-a...
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ISBN:
(纸本)9781665487788
Isolated power systems with high shares of renewables can require additional inertia as a complementary resource to assure the system operation in a dynamic safe region. This paper presents a methodology for the day-ahead Unit Commitment/ Economic Dispatch (UC/ED) for low-inertia power systems including dynamic security constraints for key frequency indicators computed by an Artificial Neural-Network (ANN)-supported Dynamic Security Assessment (DSA) tool. The ANN-supported DSA tool infers the system dynamic performance with respect to key frequency indicators following critical disturbances and computes the additional synchronous inertia that brings the system back to its dynamic security region, by dispatching Synchronous Condensers (SC) if required. The results demonstrate the effectiveness of the methodology proposed by enabling the system operation within safe frequency margins for a set of high relevance fault type contingencies while minimizing the additional costs associated with the SC operation.
In this paper, a method to calculate the maximum allowable capacity (MAC) of renewable energy sources (RESs) in terms of AC system strength is presented;the equivalent short-circuit ratio (ESCR) and the newly proposed...
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In this paper, a method to calculate the maximum allowable capacity (MAC) of renewable energy sources (RESs) in terms of AC system strength is presented;the equivalent short-circuit ratio (ESCR) and the newly proposed reactive power considered equivalent short-circuit ratio (QESCR) are considered. Unlike conventional measures, the reactive power afforded by RESs is considered when calculating the QESCR;the MAC of RESs increases because their reactive power outputs stabilize transmission networks. Based on the AC strength measures, an optimization problem to evaluate the MAC of RESs and determine their optimal locations is developed. The optimization problem is formulated in mixed-integer linear programming (MILP). To validate the effectiveness of the proposed method, it is applied to two test systems: the IEEE RTS-79 test system and a real-world Korean transmission network. The proposed method allows facile calculation of the MAC of RESs, which ensures that the transmission network remains stable.
This paper proposes a novel modelling framework for Unit Commitment (UC) studies in multi-terminal VSCconnected AC grids. Battery Energy Storage Systems (BESS) are also considered with an energy time-shifting strategy...
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This paper proposes a novel modelling framework for Unit Commitment (UC) studies in multi-terminal VSCconnected AC grids. Battery Energy Storage Systems (BESS) are also considered with an energy time-shifting strategy whose charge and discharge modes are defined within the 24-hour planning horizon to promote a high competition level of energy trading in the power grid. The entire transmission network is formulated by shift factors and power losses of AC/DC branches and VSC units are included by piecewise linear functions. Thus, this novel UC approach retains a mixed-integer linear programming model with a high modelling versatility for arbitrary hybrid power grids accommodating any number of VSC stations and BESS units. The method described in this paper enables the optimal generation scheduling combined with the charge and discharge BESS modes. Its applicability is confirmed using two case studies, one featuring a four-terminal VSCHVDC system with two BESS, and another characterised by seven VSC-connected AC systems with five BESS. To validate the proposed method, results are compared against those furnished by the classic UC representing the network through nodal equations. It is concluded that both methods favourably agree with each other as the errors are inferior to 2%. The usefulness of the proposed method to the real-time operation of AC/DC grids with BESS facilities is valuable.
With the increasing uptake of electric vehicles (EVs), the need for efficient scheduling of EV charging is becoming increasingly important. A charging station operator needs to identify charging/discharging power of t...
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With the increasing uptake of electric vehicles (EVs), the need for efficient scheduling of EV charging is becoming increasingly important. A charging station operator needs to identify charging/discharging power of the client EVs over a time horizon while considering multiple objectives, such as operating costs and the peak power drawn from the grid. Evolutionary algorithms (EAs) are a popular choice when faced with problems involving multiple objectives. However, since the objectives and constraints of this problem can be expressed using linear functions, it is also possible to come up with improvised multi-objective formulations which can be solved with exact techniques such as mixed-integer linear programming (MILP). With both approaches having their potential strengths and pitfalls, it is worth investigating their use to inform the algorithmic choices, which this study aims to address. In doing so, it makes a number of contributions to the topic, including extension of an existing EV charging problem to a multi-objective form;observing some interesting properties of the problem to improve both the MILP and EA solution approaches;and comparing the performance of MILP and EA. The study provides some useful insights into the problem, initial results and quantitative basis for selecting solution approaches, and highlights some areas of further development.
This paper proposes an optimization model for the joint planning of sectionalizing switches (SSs), including remote-controlled switches (RCSs) and manual switches (MSs), and tie lines among distribution feeders. Consi...
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This paper proposes an optimization model for the joint planning of sectionalizing switches (SSs), including remote-controlled switches (RCSs) and manual switches (MSs), and tie lines among distribution feeders. Considering a flexible installation of SSs and tie lines (i.e., deployment of SSs on both sides of a line and construction of external and internal tie lines on of a feeder), this model is conducive to developing an economic investment scheme while preserving the expected energy not supplied (EENS) reliability criterion. First, the minimum total cost of investment, maintenance and penalty for EENS is chosen as the objective function. Next, a route analysis method is introduced to analyze particular performances of different deployment schemes for RCSs, MSs, external tie lines and internal tie lines. Considering the differences between overhead lines and underground cables, the lower bound of the restoration time of each load is estimated based on optimal restoration strategies in different fault scenarios. Then, the joint planning problem is formulated as a mixed-integer linear programming (MILP) model with the aid of algebraic operations to calculate the global optimal solution. The effectiveness of the proposed model is verified for several cases in a 54-node distribution system. Numerical results indicate that the proposed planning solution merits higher economy and lower EENS as compared to the schemes offered by other prevalent methods.
Crowdshipping is a revolutionary concept of the sharing economy. In this study, two carriers are used to perform the following expedition: the truck starts from the depot to complete part of the deliveries, shares par...
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Crowdshipping is a revolutionary concept of the sharing economy. In this study, two carriers are used to perform the following expedition: the truck starts from the depot to complete part of the deliveries, shares part of the load with the crowdshipper at the relay point, and the private driver selected as the crowdshipper continues from that point onward. This study proposes the two-echelon open vehicle routing problem with crowdshipping (2EOVRP-CS) and formulates a mathematical model to determine the crowdshipper, parcel relay location, truck route, and crowdsource route. A tangible nested genetic algorithm (NGA) is proposed, and its efficiency is demonstrated by comparison with CPLEX and genetic algorithm (GA). A real case study is investigated in Xi'an city to test the applicability of the proposed model. The results show that using crowdshipping instead of truck delivery alone can save approximately 14% of the total cost and 26% of truck vehicle miles traveled (VMT). Moreover, several sensitivity analyses are performed. The results show that crowdshipping is sensitive to the detour limit and the time value of carriers. For the detour limit, after the acceptable detour distance increases by 8%, the total cost can be reduced by up to 5.94%.
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