High penetration of renewable energy source makes microgrid (MGs) be environment friendly. However, the stochastic input from renewable energy resource brings difficulty in balancing the energy supply and demand. Purchasing extra energy from macrogrid to deal with energy shortage will increase MG energy cost. To mitigate intermittent nature of renewable energy, energy trading and energy storage which can exploit diversity of renewable energy generation across space and time are efficient and cost-effective methods. But current energy storage control action will impact the future control action which brings challenge to energy management. In addition, due to MG participating energy trading as prosumer, it calls for an efficient trading mechanism. Therefore, this paper focuses on the problem of MG energy management and trading. Energy trading problem is formulated as a stochastic optimization one with both individual profit and social welfare maximization. Firstly a Lyapunov optimization based algorithm is developed to solve the stochastic problem. Secondly the double-auction based mechanism is provided to attract MG truthful bidding for buying and selling energy. Through theoretical analysis, we demonstrate that individual MG can achieve a time average energy cost close to offline optimum with tradeoff between storage capacity and energy trading cost. Meanwhile the social welfare is also asymptotically maximized under double auction. Simulation results based on real world data show the effectiveness of our algorithm.
In this paper, we investigate the minimization of the total energy cost of multiple residential households in a smart grid neighborhood sharing a load serving entity. Specifically, each household may have renewable generation, energy storage as well as inelastic and elastic energy loads, and the load serving entity attempts to coordinate the energy consumption of these households in order to minimize the total energy cost within this neighborhood. The renewable generation, the energy demand arrival, and the energy cost function are all stochastic processes and evolve according to some, possibly unknown, probabilistic laws. We develop an online control algorithm, called Lyapunov-based cost minimization algorithm (LCMA), which jointly considers the energy management and demand management decisions. LCMA only needs to keep track of the current values of the underlying stochastic processes without requiring any knowledge of their statistics. Moreover, a decentralized algorithm to implement LCMA is also developed, which can preserve the privacy of individual household owners. Numerical results based on real-world trace data show that our control algorithm can effectively reduce the total energy cost in the neighborhood. Decentralized energy management is of paramount importance in smart micro grids with renewables for various reasons including environmental friendliness, reduced communication overhead, and resilience to failures. In this context, the present work deals with distributed economic dispatch and demand response initiatives for grid-connected micro grids with high-penetration of wind power. To cope with the challenge of the wind's intrinsically stochastic availability, a novel energy planning approach involving the actual wind energy as well as the energy traded with the main grid, is introduced. A stochastic optimization problem is formulated to minimize the micro grid net cost, which includes conventional generation cost as well as the expected transaction cost incurred by wind uncertainty. To bypass the prohibitively high-dimensional integration involved, an efficient sample average approximation method is utilized to obtain a solver with guaranteed convergence. Leveraging the special infrastructure of the micro grid, a decentralized algorithm is further developed via the alternating direction method of multipliers. Case studies are tested to corroborate the merits of the novel approaches. One of the most important challenges in smart grid systems is the integration of renewable energy resources into its design. In this paper, two different techniques to mitigate the time-varying and intermittent nature of renewable energy generation are considered. The first one is the use of storage, which smooths out the fluctuations in the renewable energy generation across time. The second technique is the concept of distributed generation combined with cooperation by exchanging energy among the distributed sources. This technique averages out the variation in energy production across space. This paper analyzes the tradeoff between these two techniques. The problem is formulated as a stochastic optimization problem with the objective of minimizing the time average cost of energy exchange within the grid. First, an analytical model of the optimal cost is provided by investigating the steady state of the system for some specific scenarios. Then, an algorithm to solve the cost minimization problem using the technique of Lyapunov optimization is developed, and results for the performance of the algorithm are provided. These results show that in the presence of limited storage devices, the grid can benefit greatly from cooperation, whereas in the presence of large storage capacity, cooperation does not yield much benefit. Further, it is observed that most of the gains from cooperation can be obtained by exchanging energy only among a few energy-harvesting sources. By sharing resources among different cloud providers, the paradigm of federated clouds exploits temporal availability of resources and geographical diversity of operational costs for efficient job service. While interoperability issues across different cloud platforms in a cloud federation have been extensively studied, fundamental questions on cloud economics remain: When and how should a cloud trade resources (e.g., virtual machines) with others, such that its net profit is maximized over the long run, while a close-to-optimal social welfare in the entire federation can also be guaranteed? To answer this question, a number of important, interrelated decisions, including job scheduling, server provisioning, and resource pricing, should be dynamically and jointly made, while the long-term profit optimality is pursued. In this work, we design efficient algorithms for intercloud virtual machine (VM) trading and scheduling in a cloud federation. For VM transactions among clouds, we design a double-auction-based mechanism that is strategy-proof, individual-rational, ex-post budget-balanced, and efficient to execute over time. Closely combined with the auction mechanism is a dynamic VM trading and scheduling algorithm, which carefully decides the true valuations of VMs in the auction, optimally schedules stochastic job arrivals with different service level agreements (SLAs) onto the VMs, and judiciously turns on and off servers based on the current electricity prices. Through rigorous analysis, we show that each individual cloud, by carrying out the dynamic algorithm in the online double auction, can achieve a time-averaged profit arbitrarily close to the offline optimum. Asymptotic optimality in social welfare is also achieved under homogeneous cloud settings. We carry out simulations to verify the effectiveness of our algorithms, and examine the achievable social welfare under heterogeneous cloud settings, as driven by the real-world Google cluster usage traces. The emergence of cloud computing has established a trend towards building energy-hungry and geographically distributed data centers. Due to their enormous energy consumption, data centers are expected to have major impact on the electric power grid by significantly increasing the load at locations where they are built. Dynamic energy pricing policies in the recently proposed smart grid technology can incentivize the cloud computing controller to shift their computation load towards data centers in regions with cheaper electricity. On the other hand, distributed data centers also provide opportunities to help the smart grid to improve load balancing and robustness. To shed some light into these opportunities, this paper considers an interaction system of the smart grid, the cloud computing system, and other load devices. A nested two stage game based formulation is proposed based on the location-dependent real-time pricing policy of the smart grid. The leading player in this game is the smart grid controller that announces the relationship between the electricity price at each power bus and the total load demand at that bus. In the second stage, the cloud computing controller performs resource allocation as response to the pricing functions, whereas the other load devices perform demand side management. The objective of the smart grid controller is to maximize its own profit and perform load balancing among power buses, whereas the objective of the cloud computing controller is to maximize its own profit with respect to the location-dependent pricing functions. The optimal strategies are derived based on the backward induction principle for the smart grid controller, the cloud computing controller, and the other load devices, using convex optimization and heuristic search. Mobile data offloading through complementary network technologies such as WiFi and femtocell can significantly alleviate network congestion and enhance users' QoS. In this paper we consider a market where mobile network operators (MNOs) lease third-party deployed WiFi or femtocell access points (APs) to dynamically offload the traffic of their mobile users. We assume that each MNO can employ multiple APs and each AP can concurrently serve traffic from multiple MNOs. We design an iterative double auction mechanism that ensures the efficient operation of the market, where MNOs maximize their offloading benefits and APs minimize their offloading costs. Such a mechanism incorporates the special characteristics of the wireless network, such as the coupling of MNOs' offloading decisions and APs' capacity constraints. The proposed market scheme does not require full information about the MNOs and APs, incurs minimum communication overhead, and creates non-negative revenue for the market broker.