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Advanced spatio-temporal electric load modeling and accurate spatio-temporal load forecast are essential to both short-term operation and long-term planning of power systems. This paper explores the spatio-temporal dependencies of electric load time series. The Southern California feeder load data show that feeders which are spatially close to each other share a more similar load pattern than those located further apart. This finding motivates us to develop the vector autoregressive model and the extended dynamic spatio-temporal model to emulate the spatio-temporal correlations of the real-world electric load time series. The testing results show that both models effectively capture the spatio-temporal patterns in the real-world electric load time series. Compared to the traditional vector autoregressive model, the proposed extended dynamic spatio-temporal model not only provides more accurate spatio-temporal electric load forecast but also obtains a parsimonious description of the high dimensional dataset. 

This study attempts to establish the need for a framework to assess the impact of connected buildings in a smart community. The contribution is a software framework designed to optimize buildings and grids at a district level. The following research products are developed: (1) An innovative method to model a cluster of buildings—with people’s behavior embedded in the cluster’s dynamics—and their controls so that they can be integrated with grid operation and services; (2) a novel optimization framework to solve complex, centralized control problems for large-scale systems, leveraging convex programming approaches; and (3) a methodology to assess the impacts of connected buildings in terms of (a) the grid’s operational stability and safety and (b) buildings’ optimized energy consumption. To test the proposed framework, a large-scale simulation of a subtransmission network with three power generating stations and serving over 300 artificial buildings is conducted. 

This paper develops a two-step procedure for commercial buildings to optimize the frequency regulation service provision by leveraging the heating, ventilation, and air conditioning (HVAC) systems. Both day-ahead and real-time operations of the HVAC system are simulated by using a typical commercial building's model, the PJM market prices, and dynamic regulation signals. The simulation results show that it is beneficial for buildings to provide dynamic regulation services where the capacity reserved for regulation up and down are the same. The mean reverting characteristic of the dynamic regulation signal enables commercial buildings to increase regulation capacity with minimal impact on the comfort level of occupants. The proposed frequency regulation provisioning scheme yields a high performance score (>0.9). The simulation results also reveal that there exists a trade-off between frequency regulation performance and climate control performance of the building. Finally, the economic benefits of frequency regulation provisions of commercial buildings are analyzed. 

This paper proposes the risk-limiting unit commitment (RLUC) as the operational method to address the uncertainties in the smart grid with intelligent periphery (GRIP). Three key requirements are identified for the RLUC in GRIP. The first one requires the RLUC to be modeled as a multi-stage multi-period unit commitment problem considering power trades, operational constraints, and operational risks. The second one requires the RLUC considering the conditional prediction to achieve a globally optimal solution. It is addressed by using conditional probability in a scenario-based form. The last one requires the risk index in the RLUC to be both valid and computationally friendly, and it is tackled by the utilization of a coherent risk index and the mathematical proof of a risk chain theorem. Finally, the comprehensive RLUC in GRIP satisfying all the three requirements is solved by an equivalent transformation into a mixed integer piecewise linear programming problem. Case studies on a 9-bus system, a realistic provincial power system, and a regional power grid in China demonstrate the advantages of the proposed RLUC in GRIP. 

In this paper, an improved multi-period risk-limiting dispatch (IMRLD) is proposed as an operational method in power systems with high percentage renewables integration. The basic risk-limiting dispatch (BRLD) is chosen as an operational paradigm to address the uncertainty of renewables in this paper due to its three good features. In this paper, the BRLD is extended to the IMRLD so that it satisfies the fundamental operational requirements in the power industry. In order to solve the IMRLD problem, the convexity of the IMRLD is verified. A theorem is stated and proved to transform the IMRLD into a piece-wise linear optimization problem which can be efficiently solved. In addition, the locational marginal price of the IMRLD is derived to analyze the effect of renewables integration on the marginal operational cost. Finally, two numerical tests are conducted to validate the IMRLD. 

This paper presents a three-phase iterative direct current optimal power flow (DCOPF) algorithm with fictitious nodal demand. Power losses and realistic distribution system operating constraints such as line flow limits and phase imbalance limits are carefully modeled in the DCOPF formulation. The definition of locational marginal prices (LMPs) is extended to three-phase distribution systems. The three-phase LMP decomposition is derived based on the Lagrangian function. The proposed algorithm is implemented in an IEEE test case and compared with three-phase alternating current optimal power flow (ACOPF) algorithm. The simulation results show that the proposed DCOPF algorithm is effective in coordinating the operations of distributed energy resources (DERs) and managing phase imbalance and thermal overloading. The proposed iterative three-phase DCOPF algorithm provides not only a computationally efficient solution but also a good approximation to the ACOPF solution.