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Adoption of renewable energy in power grids introduces stability challenges in regulating the operation frequency of the electricity grid. Thus, electrical grid operators call for provisioning of frequency regulation services from end-user customers, such as data centers, to help balance the power grid’s stability by dynamically adjusting their energy consumption based on the power grid’s need. As renewable energy adoption grows, the average reward price of frequency regulation services has become much higher than that of the electricity cost. Therefore, there is a great cost incentive for data centers to provide frequency regulation service. Many existing techniques modulating data center power result in significant performance slowdown or provide a low amount of frequency regulation provision. We present PowerMorph , a tight QoS-aware data center power-reshaping framework, which enables commodity servers to provide practical frequency regulation service. The key behind PowerMorph  is using “complementary workload” as an additional knob to modulate server power, which provides high provision capacity while satisfying tight QoS constraints of latency-critical workloads. We achieve up to 58% improvement to TCO under common conditions, and in certain cases can even completely eliminate the data center electricity bill and provide a net profit. 

Micro-grids’ operations offer local reliability; in the event of faults or low voltage/frequency events on the utility side, micro-grids can disconnect from the main grid and operate autonomously while providing a continued supply of power to local customers. With the ever-increasing penetration of renewable generation, however, operations of micro-grids become increasingly complicated because of the associated fluctuations of voltages. As a result, transformer taps are adjusted frequently, thereby leading to fast degradation of expensive tap-changer transformers. In the islanding mode, the difficulties also come from the drop in voltage and frequency upon disconnecting from the main grid. To appropriately model the above, non-linear AC power flow constraints are necessary. Computationally, the discrete nature of tap-changer operations and the stochasticity caused by renewables add two layers of difficulty on top of a complicated AC-OPF problem. To resolve the above computational difficulties, the main principles of the recently developed “l1-proximal” Surrogate Lagrangian Relaxation are extended. Testing results based on the nine-bus system demonstrate the efficiency of the method to obtain the exact feasible solutions for micro-grid operations, thereby avoiding approximations inherent to existing methods; in particular, fast convergence of the method to feasible solutions is demonstrated. It is also demonstrated that through the optimization, the number of tap changes is drastically reduced, and the method is capable of efficiently handling networks with meshed topologies. 

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 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. 

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.