This study presents a summary of green building research through a bibliometric approach. A total of 2980 articles published in 2000–2016 were reviewed and analyzed. The results indicated that green building research had been concentrated on the subject categories of engineering, environmental sciences & ecology, and construction & building technology, and the keywords ‘building envelope’ and ‘living wall’ obtained citation bursts in the recent years. Additionally, based on the cluster analysis and content analysis, the hot research topics were identified: green and cool roof, vertical greening systems, water efficiency, occupants’ comfort and satisfaction, financial benefits of green building, life cycle assessment and rating systems, green retrofit, green building project delivery, and information and communication technologies in green building. Knowledge gaps were detected in the areas of corporate social responsibility, the validation of real performance of green building, the ICT application in green building, as well as the safety and health risks in the construction process of green projects. Future research directions are recommended to fill these gaps and extend the body of green building research.

State estimation is a fundamental function in modern energy management system (EMS), but its results may be vulnerable to false data injection attacks (FDIA). FDIA is able to change the estimation results without being detected by the traditional bad data detection algorithms. In this paper, we propose an accurate and computational attrac-tive approach for FDIA detection. We first rely on the low rank characteristic of the measurement matrix and the sparsity of the attack matrix to reformulate the FDIA detection as a matrix separation problem. Then, three algo-rithms that solve for this problem are presented and com-pared, including the traditional Augmented Lagrange Mul-tipliers (ALM), the Low Rank Matrix Factorization (LMaFit) and the proposed new Go Decomposition (GoDec). Numerical simulation results show that our GoDec outperforms the other two alternatives and demon-strates a much higher computational efficiency. Furthermore, GoDec is shown to be able to handle measurement noise and applicable for large-scale attacks.

To incorporate the superiority of both stochastic and robust ap-proaches, a data-driven stochastic optimization is employed to solve the security constrained unit commitment model. This ap-proach makes the full use of the historical data information to generate a set of possible probability distributions for wind pow-er output and optimizes the unit commitment under the worst-case probability distribution. However, this model suffers from huge computational burden, as a large number of scenarios are considered. To resolve this problem, a duality-free decomposi-tion method is proposed in this paper. This approach does not require the step of dualization, which can save a large set of dual variables and constraints, and therefore reduces the computa-tional burden. In addition, due to the special structure of the second stage, the proposed method can be decomposed into in-dependent sub-problems that are solved in parallel, which fur-thermore improves the computational efficiency. A numerical study on an IEEE 118-bus system with practical data of a wind power system has demonstrated the effectiveness of the proposal.

This paper presents a multi-period active distribution network planning (ADNP) with distributed generation. While subject to both planning and operation constraints, the objective function of the proposed ADNP is to minimize the total cost of investment and operation. The paper proposes a multi-stage stochastic optimization model to address the uncertainties associated with the distributed generation (DG). In order to characterize the uncertainties over several periods, the decisions are made sequentially by only using the present-stage information. Furthermore, a nested decomposition method is proposed which applies the stochastic dual dynamic integer programming (SDDIP) method to address computational intractabilities. The presented numerical results and discussions on a 33-bus distribution system verify the effectiveness of the proposed model and its solution method.

Today’s power distribution system is changing to a power-electronics-enabled distribution system, especially with the increasing penetration of distributed energy resources (DERs). To monitor and manage those electronic devices and DERs at the grid edge, the advanced metering infrastructure (AMI) with two-way communications presents great potential. At present, extensive research explores the upstream communication from smart meters to electric utilities (e.g., meter reading) but few examine the downstream communication from the utilities to smart meters (e.g., meter pinging). This paper discusses the AMI two-way communication and its recent industrial practice in the U.S., especially for applying the smart meter pinging functionality to monitor grid-edge devices and DERs. This paper then develops the two-way communication model and the network calculus method to quantify the impact of the two-way communication on the AMI network. In the end, the proposed method is validated with ns-3 simulation using the modified 13-node test feeder and real-world feeder systems.

Energy flow calculation is a fundamental problem of the integrated power and gas system (IPGS) operation and planning. However, the nonlinear gas flow model introduces major challenges to the energy flow calculation. In this paper, we propose a tractably convex optimization model to solve the energy flow problem in IPGSs. It is demonstrated that the proposed optimization model has the same optimal solution as the original nonlinear steady energy flow model. Also, piecewise linearization is adopted to tightly linearize the nonlinear objective function of the model, which transforms the formulated convex optimization into a linear program one. Thus, the computation complexity of the proposed energy flow model is significantly reduced as compared with the existing methods. In addition, the proposed model can be extended to probabilistic energy flow estimation. Extensive case studies are conducted to validate the effectiveness of the proposed energy flow model using three IPGSs.