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Byconsuming35% of the final global energy, buildings are among major contributors to greenhouse gas emissions and global warming. If the economic justification challenge is addressed, zero energy buildings (ZEB), which are defined as buildings that generate as much renewable-based energy as they consume annually, can be a promising solution for energy efficiency improvement in the building sector. In this article, we propose a stochastic ZEB device sizing model considering uncertain parameters (i.e., building's electrical and thermal demands, solar irradiation, and outdoor temperature) and their correlation. The proposed model finds the optimal size of the thermal and electrical devices considering their nonlinear behaviors and temperature-dependent dynamics. Furthermore, two parallel computing-based solution algorithms (i.e., parallelism in the algebraic level using Schur complement decomposition and parallelism in the problem (scenario) level by progressive hedging) are proposed to solve the stochastic model. Using the real historical data, numerical studies on the Woodward library building, located on the University of British Columbia campus, illustrate the efficacy of the solution algorithms to handle large-scale nonlinear programming models with more than 32.8 million variables, which is the largest one reported in the literature so far. [ABSTRACT FROM AUTHOR]

To design a reliable and secure power system, it is necessary to have enough transmission capacity. The solution of transmission expansion planning (TEP) problem determines cost-optimal investment in future transmission equipment. In this paper, we propose a new global solver, named Global-TEP, for the TEP problem with an AC network representation (ACTEP), which is a mixed-integer nonlinear programming problem. The proposed solver is based on second-order cone relaxation, enhanced relaxation tightening constraints, and optimization-based/feasibility-based bound tightening techniques. Multiple enhanced relaxation tightening constraints are incorporated into the mixed-integer second-order cone relaxation of TEP in order to obtain a very strong relaxation as the lower bounding problem. In addition, a novel feasibility-based bound tightening technique is proposed to tighten the bounds of decision variables in a considerably short runtime. Finally, introducing a novel application of optimization-based bound tightening technique, Global-TEP is constructed that can solve the ACTEP problem efficiently with a guaranteed optimality gap. As illustrated by numerical case studies, Global-TEP is more scalable, more flexible, and much faster than the available global solvers. [ABSTRACT FROM AUTHOR]

This paper proposes a new bi-objective optimization model, trading-off cost and environmental impacts, for sizing the key electrical and thermal devices in a zero energy building (ZEB), i.e., a building that roughly generates as much renewable energy as it consumes annually. A salient novel feature is the consideration of the environmental impacts, computed through a rigorous life cycle assessment approach, of buying electricity from the grid and manufacturing devices. Furthermore, an enhancement of the proposed model, as compared to the existing models, is to prioritize storing the ZEB excess of energy rather than selling it to the grid. The proposed solution approach of the initial mixed-integer nonlinear programming model relies on McCormick relaxation linearization to obtain a more tractable mixed-integer linear model. An augmented ϵ-constraint method is applied to solve the obtained bi-objective model. Finally, considering the building owners’ willingness-to-pay for environmental impacts, a decision-making criterion is proposed to select the optimal size of the devices among all non-dominated solutions of the Pareto front. [ABSTRACT FROM AUTHOR]

The important focus of the energy strategy of the European Union relies on the concept of zero energy building (ZEB), which is, by definition, a building that roughly produces yearly as much renewable energy as it consumes. This article proposes an enhanced mixed-integer nonlinear programming model for optimal sizing of photovoltaic (PV) and battery energy storage systems to comply with the definition of a ZEB. A salient novel feature of the proposed model is that it factors in the environmental impacts, computed through rigorous life cycle assessment methodology, of buying electricity from the grid and manufacturing battery and PV systems. Furthermore, an adjustable parameter is introduced to make the model adaptive from the perspective of the building owner's willingness-to-pay for environmental impacts. The proposed model is then rigorously reformulated, managing to accumulate its nonlinearity in only one constraint per time interval. Eventually, the reformulated model is linearized to a mixed-integer linear programming model using the McCormick relaxation technique. The case study conducted on archetypal buildings in Luxembourg reveals that the proposed McCormick-based linear model is able to provide high accuracy results with reasonable computational effort. [ABSTRACT FROM AUTHOR]

One important task in power distribution system expansion planning is feeder routing (FR), which is to determine the optimal route from a medium voltage substation to load points and the optimal size of the conductors to be installed. This paper proposes a novel graph-based model for the resilient feeder routing problem using geographical information system (GIS). We show that incorporating GIS data enhances the FR solution optimality. Moreover, by introducing a representing graph, radiality of the planned network is guaranteed. A second-order cone programming (SOCP) model is used to model power flows through feeders. Since the representing graph ensures radiality, the SOCP model is exact. The uncertainty of rooftop solar generations and demand forecasting errors are taken into account, and a stochastic programming-based solution algorithm is developed. The proposed model represents practical aspects such as economic objectives (installation cost, power losses, resiliency), technical constraints (voltage limits, radiality constraint, reliability), and geographical constraints. The efficiency of the algorithm is demonstrated using three case studies: a small test system, a realistic one, and a synthetic large test system. [ABSTRACT FROM AUTHOR]

In this article, we present a data-driven nonparametric chance-constrained optimization for microgrid energy management. The proposed approach imposes no assumption on probability density and distribution functions of solar generation and load. Adaptive kernel density estimator is utilized to construct a confidence set for each random parameter based on the historical data. The constructed confidence sets encompass the ambiguous true distribution and density functions. The concept of $\phi $ -divergence tolerance is applied to compute the distance between the estimated and true probability distribution functions (PDF)s. The estimated distributions are used to formulate a set of data-driven nonparametric chance constraints and model system/component restrictions. To account for the impact of errors in the forecast distributions on system economics and security, confidence levels of the chance constraints are adjusted with respect to pointwise errors of the estimated PDFs. This adjustment ensures that the microgrid chance constraints are satisfied with a predetermined confidence level even if the true realizations of solar generation and load do not exactly fit on the estimated PDFs. The chance constraints are converted into algebraic constraints. Numerical results show the effectiveness of the proposed approach for microgrid management. [ABSTRACT FROM AUTHOR]

Collaborative operation of electricity transmission and distribution systems improves the economy and reliability of the entire power system. However, this is a challenging problem given that transmission system operators (TSOs) and distribution system operators (DSOs) are autonomous entities that are unwilling to reveal their commercially sensitive information. This paper presents a decentralized decision-making algorithm for collaborative TSO+DSO optimal power flow (OPF) implementation. The proposed algorithm is based on analytical target cascading for multilevel hierarchical optimization in complex engineering systems. A local OPF is formulated for each TSO/DSO taking into consideration interactions between the transmission and distribution systems while respecting autonomy and information privacy of TSO and DSOs. The local OPF of TSO is solved in the upper-level of hierarchy, and the local OPFs of DSOs are handled in the lower-level. A diagonal quadratic approximation (DQA) and a truncated DQA are presented to develop iterative coordination strategies in which all local OPFs are solved in a parallel manner with no need for a central coordinator. This parallel implementation significantly enhances computations efficiency of the algorithm. The proposed collaborative TSO+DSO OPF is evaluated using a 6-bus and the IEEE 118-bus test systems, and promising results are obtained. [ABSTRACT FROM AUTHOR]

This paper presents a decentralized solution algorithm for network-constrained unit commitment (NCUC) in multiregional power systems. The proposed algorithm is based on our previous work in which a local NCUC was formulated for each control entity (i.e., region) and an analytical target cascading (ATC) based distributed but partially parallelized algorithm requiring a central coordinator was presented. The primary objective of this paper is to present a decentralized approach that relaxes the need for any form of central coordinator in ATC and allows fully parallelized solutions of the local NCUCs. To achieve this objective, we formulate a bilevel optimization problem for each control entity. While the upper level solves the NCUC problem of the control entity, the lower level seeks to coordinate the control entity with its neighboring regions. The lower level is a convex optimization, which can be further replaced in the upper level problem by the Karush–Kuhn–Tucker conditions. The control entities communicate directly with each other and synchronously solve their local NCUCs. Having no need for any form of central coordinator, the proposed algorithm is potentially less vulnerable to cyber-attacks and communication failures than the distributed methods utilizing a coordinator. [ABSTRACT FROM AUTHOR]