We consider Data Centers (DCs) as flexible loads that can alter their power consumption to alleviate congestion in the electric power network. We model DCs using a queuing-theoretic view and we form a Quality of Service (QoS)-based cost function that signifies how well a DC can carry out its workload given an amount of active servers. We integrate DCs in a centralized economic dispatch problem that determines, apart from power generation, DC workload shifting and server utilization, while respecting transmission line constraints. We further present a tractable decentralized formulation obtained via Lagrangian decomposition, which we solve using a dual gradient ascent algorithm. Experimental results on a standard power network explore the system-wide benefits of DC flexibility in “coupled” data and power networks, emphasizing on the trade-offs between the DC location, QoS, and efficiency.

This special issue brings together recent research on the efficient use of energy in commercial, residential, and other types of buildings and facilities.

Marginal-cost-based dynamic pricing of electricity services, including real power, reactive power, and reserves, may provide unprecedented efficiencies and system synergies that are pivotal to the sustainability of massive renewable generation integration. Extension of wholesale high-voltage power markets to allow distribution network connected prosumers to participate, albeit desirable, has stalled on high transaction costs and the lack of a tractable market clearing framework. This paper presents a distributed, massively parallel architecture that enables tractable transmission and distribution locational marginal price (T&DLMP) discovery along with optimal scheduling of centralized generation, decentralized conventional and flexible loads, and distributed energy resources (DERs). DERs include distributed generation; electric vehicle (EV) battery charging and storage; heating, ventilating, and air conditioning (HVAC) and combined heat & power (CHP) microgenerators; computing; volt/var control devices; grid-friendly appliances; smart transformers; and more. The proposed iterative distributed architecture can discover T&DLMPs while capturing the full complexity of each participating DER's intertemporal preferences and physical system dynamics.

To accommodate the increasing presence of volatile and intermittent renewable energy sources in power generation, independent system operators (ISO) offer opportunities for demand side regulation service (RS) so as to stabilize the grid load. These power market features allow the demand side to earn monetary credits by modulating its power consumption dynamically following an RS signal broadcast by ISO. This paper studies the capacities and benefits of a major potential demand side, the data center, to provide RS. We propose a dynamic control policy that modulates the data center power consumption in response to ISO requests by leveraging server power capping techniques and various server power states. Results demonstrate that using our policy, data centers can provide fast reserves in quantities that are substantial proportions (around 50%) of their average energy consumption, with no major deterioration in quality of service (QoS). By doing so, data centers decrease their energy costs around 50%, while providing the ISOs and the society in general with cost effective demand side reserves that render massive renewable generation adoption affordable.

Today's US power markets offer new opportunities for the energy consumers to reduce their energy costs by first promising an average consumption rate for the next hour and then by following a regulation signal broadcast by the independent system operators (ISOs), who need to match supply and demand in real time in presence of volatile and intermittent renewable energy generation. This paper leverages the power regulation capabilities of the servers so as to enable the data centers to participate in these emerging power markets. As the data center energy consumption continues to grow, proposed participation in the power markets has the promise to achieve significant monetary savings. The paper first solves a data center regulation service (RS) optimization problem to determine the optimal average power consumption and regulation quantity that minimize the energy cost. We then propose a dynamic server power capping technique to modulate the real-time power consumption in response to ISO requests while maintaining the desired quality-of-service (QoS). Experiments on a real-life server demonstrate that our technique can reduce the energy cost by 29% on average compared to using a fixed power cap.

A new parametric class of scheduling and routing policies for open multiclass queueing networks is proposed. We establish their stability and show they are amenable to distributed implementation using localized state information. We exploit our earlier work in (Ref.1) to select appropriate parameter values and outline how optimal parameter values can be computed. We report numerical results indicating that we obtain near-optimal policies (when the optimal can be computed) and significantly outperform heuristic alternatives.

We consider planning and control of production systems in a long supply chain just-in-time (JIT) manufacturing environment with stochastic disturbances. We propose an iterative algorithm where a master problem determines tentative production requirement targets for each cell/focused factory in a cellular manufacturing plant. Subproblems-one for each cell and for each period/time-bucket in the planning horizon-are solved after each iteration to determine part-type and period specific lead times needed to achieve the master problem's tentative production targets. In addition, lead time gradients with respect to production targets are estimated by subproblems and passed on to the master problem. This allows the master problem to learn about the behavior of cell and part type specific lead times as a function of capacity utilization and production mix. Under reasonable conditions, the proposed iterative algorithm can learn this behavior with arbitrary accuracy in the neighborhood of the optimal solution, and thus generate a production plan that minimizes inventory and backlog costs, and optimizes JIT objectives. Computational results are provided to illustrate possible efficiency gains.