We study the problem of optimally and simultaneously sizing solar photovoltaic (PV) and storage capacity in order to partly or completely offset grid usage. While prior work offers some insights, researchers typically consider only a single sizing approach. In contrast, we use a firm theoretical foundation to compare and contrast sizing approaches based on robust simulation, robust optimization, and stochastic network calculus. We evaluate the robustness and computational complexity of these approaches in a realistic setting to provide practical, robust advice on system sizing.

To comply with sustainability goals, many companies buy green energy to serve their energy demand. This is typically done by engaging in bilateral power purchase agreements (PPA) with renewable energy producers (REP). A PPA can be flexibly structured, but the core principle is that a buyer (company) agrees to buy future energy production of a seller (REP) at an agreed-upon fixed price. PPAs are financially attractive for sellers, providing price certainty, unlike trading in electricity markets. However, PPAs can bring quantity uncertainty for buyers due to the uncertainty of future green energy delivery. This uncertainty in the long-term endangers sustainability targets, and in the short-term complicates reliable and cost-efficient demand matching. Thus, multiple strategies have been used in PPAs to encourage sellers to provide accurate and good-faith predictions of their short-term and longer-term future production. Yet, it has been shown that REPs can have incentives to misreport predicted values. This has discouraged some companies from engaging in PPAs. In this paper, we first investigate how PPA structure and pricing can incentivize REPs to provide more reliable predictions. This shifts the risk of production uncertainty to REPs, increasing the chance that REPs adopt batteries. We further study how having batteries for REPs affects their own revenue as well as the reliability of their energy predictions for buyers. We use analytical and simulation approaches to propose a decision tree for a win-win PPA structure, which improves reliability for buyers while maintaining profitability for REPs.

The wide range of performance characteristics of storage technologies motivates the use of a hybrid energy storage system (HESS) that combines the best features of multiple technologies. However, HESS design is complex, in that it involves the choice of storage technologies, the sizing of each storage element, and deciding when to charge and discharge each underlying storage element (operating strategy). We formulate the problem of jointly optimizing the sizing and the operating strategy of an HESS that can be used for a large class of applications and storage technologies. Instead of a single set of storage element sizes, our approach determines the Pareto-optimal frontier of the sizes of the storage elements along with the corresponding optimal operating strategy. Thus, as long as the performance objective of a storage application (such as an off-grid microgrid) can be expressed as a linear combination of the underlying storage sizes, the optimal vector of storage sizes falls somewhere on this frontier. We present two case studies to illustrate our approach, demonstrating that a single storage technology is sometimes inadequate to meet application requirements, unlike an HESS designed using our approach. We also find simple, near-optimal, and practical operating strategies for these case studies, which allows us to gain several new engineering insights.

Capacity and buffer sizes are critical design parameters in schedulers which multiplex many flows. Previous studies show that in an asymptotic regime, when the number of traffic flows N goes to infinity, the choice of scheduling algorithm does not have a big impact on performance. We raise the question whether or not the choice of scheduling algorithm impacts the capacity and buffer sizing for moderate values of N (e.g., few hundred). For Markov-modulated On-Off sources and for finite N, we show that the choice of scheduling is influential on (1) buffer overflow probability, (2) capacity provisioning, and (3) the viability of network decomposition in a non-asymptotic regime. This conclusion is drawn based on numerical examples and by a comparison of the scaling properties of different scheduling algorithms. In particular, we show that the per-flow capacity converges to the per-flow long-term average rate of the arrivals with convergence speeds ranging from O (√log N/N) to O(1/N) depending on the scheduling algorithm. This speed of convergences of the required capacities for different schedulers (to meet a target buffer overflow probability) is perceptible even for moderate values of N in our numerical examples.

The high variability of solar power due to intrinsic diurnal variability, as well as additional stochastic variations due to cloud cover, have made it difficult for solar farms to participate in electricity markets that require pre-committed constant power generation. We study the use of battery storage to 'firm' solar power, that is, to remove variability so that such a pre-commitment can be made. Due to the high cost of storage, it is necessary to size the battery parsimoniously, choosing the minimum size to meet a certain reliability guarantee. Inspired by recent work that identifies an isomorphism between batteries and network buffers, we introduce a new model for solar power generation that models it as a stochastic traffic source. This permits us to use techniques from the stochastic network calculus to both size storage and to maximize the revenue that a solar farm owner can make from the day-ahead power market. Using a 10-year of recorded solar irradiance, we show that our approach attains 93% of the maximum revenue in a summer day that would have been achieved in daily market had the entire solar irradiance trace been known ahead of time.

We study how the choice of packet scheduling algorithms influences end-to-end performance on long network paths. Taking a network calculus approach, we consider both deterministic and statistical performance metrics. A key enabling contribution for our analysis is a significantly sharpened method for computing a statistical bound for the service given to a flow by the network as a whole. For a suitably parsimonious traffic model we develop closed-form expressions for end-to-end delays, backlog, and output burstiness. The deterministic versions of our bounds yield optimal bounds on end-to-end backlog and output burstiness for some schedulers, and are highly accurate for end-to-end delay bounds.

We seek to provide an analytical answer whether the impact of link scheduling algorithms on end-to-end delays diminishes on long network paths. The answer is provided through a detailed multi-hop delay analysis, which is applicable to a broad class of scheduling algorithms, and which can account for statistical multiplexing. The analysis is enabled by two contributions: (1) We derive a function that can characterize the available bandwidth at a buffered link for various scheduling algorithms. This characterization is sharp enough to provide necessary and sufficient conditions for satisfying worst-case delay bounds at a single link; (2) We obtain end-to-end delay bounds by solving an optimization problem, in which the service received on a multi-hop path is subsumed into a single function. Since our analysis captures the properties of a broad group of schedulers in a single parameter, it can provide insight how the choice of scheduling algorithms impacts end-to-end delay bounds. An important finding of this paper is that schedulers may exhibit noticeable performance differences which persist in a network setting with long paths.

We seek to provide an analytical answer whether the impact of the selection of link scheduling algorithms diminishes on long network paths. The answer is provided through a detailed multi-node delay analysis, which is applicable to a broad class of scheduling algorithms, and which can account for statistical multiplexing. The analysis is enabled by two contributions: (1) We derive a function that can characterize the available bandwidth at a node for various scheduling algorithms. The function has an accuracy that recovers necessary and sufficient conditions for satisfying worst-case delay bounds at a single node, (2) We obtain end-to-end delay bounds by providing an explicit solution to an optimization problem, in which the service received at multiple nodes is subsumed into a single function. By presenting a unified analysis that captures the properties of a broad group of schedulers in a single parameter, we can provide insight how the choice of scheduling algorithms impacts end-to-end delay bounds. An important finding of this paper is that some schedulers show noticeable performance differences which persist in a network setting with long paths.

In a SEnsor Network with Mobile Agents (SENMA) is an architecture proposed for large scale sensor networks. In sensor networks, a fraction of the packets generated by only part of the sensors is sufficient to provide a good estimation of all network information. SENMA uses this inherent redundancy. However, the performance of such system is limited by estimation errors and packet errors due to collision. Estimation error is a major problem in applications such as habitat monitoring in which information is a function of node position. In this paper, we first derive the equations of distortion in a general scenario and use this model to compare the estimation error for regular networks. Then by obtaining four best node positions for estimation error reduction, minmax distortion for estimation in a square area is achieved. In addition, an opportunistic MAC is proposed that considers both collision effects and estimation error based on Channel State Information (CSI). As the simulation results show the proposed scheme achieves an optimum estimation with minimum collision and energy consumption.

Most of the energy efficient routing algorithms proposed for sensor networks are reactive routing algorithms. By definition, reactive algorithms are those in which routes are constructed whenever there is data transmission. In such algorithms, to transmit data packets from a certain transmitter to the corresponding receiver, efficient route is constructed using RREQ delivery. This delivery is performed via flooding and modified flooding algorithms. One of the most important criteria of such flooding algorithm is not to miss the best final route during RREQ delivery. Most of major algorithms ignored this fact and instead tried to minimize energy consumption during flooding algorithm. But constructing an energy efficient final route is more important than minimizing energy consumption during RREQ delivery especially for static networks. In this paper, first analytical expressions for expected transmission time in exponential backoff system are derived. Afterwards using the mentioned analytical expressions, an efficient RREQ delivery flooding strategy is proposed to improve the optimality of the final route. The proposed algorithm concommited with our previous power control algorithm results in an efficient routing algorithm with improvement in all parameters from state of the art routing algorithms for sensor networks. Simulation results corroborate the claim

In this paper a combination of routing and power management within the context of wireless ad-hoc sensor networks is considered. Using lower layer information (e.g. collision that is considered in MAC layer) is another novel concern which is used in routing decisions. More specifically in this paper we introduce a cross-layer design in which each node dynamically chooses the number of its neighbors and adjusts its power just enough to reach its farthest determined neighbor. In this algorithm the power of transmission and collision are used as metrics for routing and the number of neighbors in which the total energy consumption in data transmission will be the minimum is selected.

Flooding algorithm is one of the most significant algorithms used in sensor networks. Although simple, this algorithm causes a large amount of energy and bandwidth to be wasted. The most important application of flooding is RREQ flooding in initial step of most routing algorithms. Although simple, this algorithm causes a large amount of energy and bandwidth to be wasted. Most previous efficient flooding algorithms use location information, which is impossible for simple node in sensor network. Some others are not suitable for RREQ flooding due to eliminating redundant retransmissions. We present a modified flooding that simultaneously decreases energy consumption as well as network delay. This flooding algorithm is a form of cross layer algorithm which uses physical layer information to be more efficient in time and energy. It is shown that the proposed algorithm can save a significant amount of energy while reducing the settling time delay

In this paper a modified neighbor aware medium-access control protocol is proposed designed for wireless sensor networks. Wireless sensor networks are generally comprised of battery powered nodes. These nodes are densely deployed in an ad-hoc structure. The main objective for sensor networks is low power consumption while latency is less important. This leads different MAC layer design for wireless sensor networks compared to conventional ad hoc protocols. In this paper we propose a neighbor aware medium access control to reduce power consumption. This protocol is derived from modified distributed mediation device (MDMD) protocol and uses useful properties of this protocol while improves the performance of this protocol especially in dense environments. We add a neighbor aware dynamic scheduling scheme to MDMD protocol to reduce power consumption. We compare this modified protocol with sensor-MAC (S-MAC) and MDMD protocol.