Yashar Ghiassi-Farrokhfal

Toward a Realistic Performance Analysis of Storage Systems in Smart Grids

Energy storage devices (ESDs) have the potential to revolutionize the electricity grid by allowing the smoothing of variable-energy generator output and the time-shifting of demand away from peak times. A common approach to study the impact of ESDs on energy systems is by modeling them as electric circuits in simulations. Although recent circuit models are becoming more accurate, to obtain statistically valid results, extensive simulations need to be run. In some cases, existing datasets are not large enough to obtain statistically significant results. The impact of ESDs on energy systems has also been recently studied using analytical methods, but usually by assuming ideal ESD behavior, such as infinite ESD charging and discharging rates, and zero self-discharge. However, real-life ESDs are far from ideal. We investigate the effect of nonideal ESD behavior on system performance, presenting an analytical ESD model that retains much of the simplicity of an ideal ESD, yet captures many (though not all) nonideal behaviors for a class of ESDs that includes all battery technologies and compressed air energy storage systems. This allows us to compute performance bounds for systems with nonideal ESDs using standard teletraffic techniques. We provide performance results for five widely used ESD technologies and show that our models can closely approximate numerically computed performance bounds.

On the Impact of Link Scheduling on End-to-End Delays in Large Networks

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.

Output characterization of constant bit rate traffic in FIFO networks

We provide an analytical proof that the departure rate of a CBR flow at an overloaded link with FIFO buffers is proportional to the flows share of the total offered load at the link. This property of FIFO scheduling was recently validated in in a series of traffic measurement experiments. An extension of the analysis to a multi-node scenario shows that the output rate of a flow in a network with many overloaded FIFO switches approaches the pessimistic values given by blind multiplexing.

Optimal Design of Solar PV Farms With Storage

We consider the problem of allocating a capital budget to solar panels and storage to maximize the expected revenue in the context of a large-scale solar farm participating in an energy market. This problem is complex due to many factors. To begin with, solar energy production is stochastic, with a high peak-to-average ratio, thus the access link is typically provisioned at less than peak capacity, leading to the potential waste of energy due to curtailment. The use of storage prevents power curtailment, but the allocation of capital to storage reduces the amount of energy produced. Moreover, energy storage devices are imperfect. A solar farm owner is thus faced with two problems: 1)deciding the level of power commitment and 2) the operation of storage to meet this commitment. We formulate two problems corresponding to two different power commitment approaches, an optimal one and a practical one, and show that the two problems are convex, allowing efficient solutions. Numerical examples show that our practical power commitment approach is close to optimal and also provide several other engineering insights.

Solar Power Shaping: An Analytical Approach

The focus of our work is the use of an energy storage system (ESS) to integrate solar energy generators into the electrical grid. Although, in theory, an ESS allows intermittent solar power to be shaped to meet any desired load profile, in practice, parsimonious ESS dimensioning is challenging due to the stochastic nature of generation and load and the diversity and high cost of storage technologies. Existing methods for ESS sizing are based either on simulation or on analysis, both of which have shortcomings. Simulation methods are computationally expensive and depend on the availability of extensive data traces. Existing analytical methods tend to be conservative, overestimating expensive storage requirements. Our key insight is that solar power fluctuations arise at a few distinct time scales. We separately model fluctuations in each time scale, which allows us to accurately estimate ESS performance and efficiently size an ESS. Numerical examples with real data traces show that our model and analysis are tight.

Joint Optimal Design and Operation of Hybrid Energy Storage Systems

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.

Using a diversity scheme to reduce energy consumption in wireless sensor networks

In this paper, a new method is proposed utilizing a diversity scheme to reduce power consumption in large scale sensor networks. Sensor networks are composed of large number of battery powered nodes. Energy consumption is the most important design objective in sensor networks while delay and throughput are taken less into account. Wireless transmission is the other important characteristic of these networks. Small-scale fading decreases wireless communication performance. In a fading channel higher SNRs is needed and consequently more energy is consumed in fading channels. Another important characteristic of sensor networks is the necessity of fault tolerant protocols. Node-to-node links are unreliable because of the insatiability of nodes and therefore the corresponding end-to-end links are unreliable. Most of the sensor network routing protocols propose path redundancy in order to guarantee a reliable packet delivery. Path redundancy makes some copies of a packet to receive to destination. This is a type of diversity and we benefit from this diversity to reduce transmit power and consequently decrease average network power consumption which it results in prolonging network life time

Cross-layer flooding for sensor networks without location information

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

Using Storage to Minimize Carbon Footprint of Diesel Generators for Unreliable Grids

Although modern society is critically reliant on power grids, modern power grids are subject to unavoidable outages. The situation in developing countries is even worse, with frequent load shedding lasting several hours a day due to a large power supply-demand gap. A common solution for residences is, therefore, to back up grid power with local generation from a diesel generator (genset). To reduce carbon emissions, a hybrid battery-genset is preferable to a genset-only system. Designing such a hybrid system is complicated by the tradeoff between cost and carbon emission. Toward the analysis of such a hybrid system, we first compute the minimum battery size required for eliminating the use of a genset, while guaranteeing a target loss of power probability for an unreliable grid. We then compute the minimum required battery for a given genset and a target-allowable carbon footprint. Drawing on recent results, we model both problems as buffer sizing problems that can be addressed using stochastic network calculus. Specifically, a numerical study shows that, for a neighborhood of 100 homes, we are able to estimate the storage required for both the problems with a fairly small margin of error compared to the empirically computed optimal value.

Does Link Scheduling Matter on 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.