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.
- Y. Ghiassi-Farrokhfal, F. Kazhamiaka, C. Rosenberg, and S. Keshav, Optimal Design of Solar PV Farms with Storage, IEEE Transactions on Sustainable Energy, October 2015.
Towards 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.
- Y. Ghiassi-Farrokhfal, S. Keshav, and C. Rosenberg, Towards a Realistic Performance Analysis of Storage Systems in Smart Grids, IEEE Transactions on Smart Grids, Vol 6, No. 1, January 2015, pp. 402-410.
Energy storage and regulation
Electric system operators rely on regulation services to match the total system supply to the total system load in quasi real-time. The regulation contractual framework requires that a regulation unit declares its regulation parameters at the beginning of the contract, the operator guarantees that the regulation signals will be within the range of these parameters, and the regulation unit is rewarded proportionally to what it declares and what it supplies. We study how this service can be provided by a unit with a non-ideal storage. We consider two broad classes of storage technologies characterized by different state of charge evolution equations, namely batteries and flywheels. We first focus on a single contract, and obtain formulas for the upward and downward regulation parameters that a unit with either a battery or a flywheel should declare to the operator to maximize its reward. We then focus on a multiple contract setting and show how to analytically quantify the reward that such a unit could obtain in successive contracts. We quantify this reward using bounds and expectation, and compare our analytical results with those obtained from a dataset of real-world regulation signals. Finally, we provide engineering insights by comparing different storage technologies in terms of potential rewards for different contract durations and parameters.
- D. Fooladivanda, C. Rosenberg, and S. Garg, “Energy Storage and Regulation: An Analysis,” in Smart Grid, IEEE Transactions on , pp.1-11.
- D. Fooladivanda, C. Rosenberg, and S. Garg. An Analysis of Energy Storage and Regulation, Proc. IEEE Smart Grid Communications, November 2014.
We focus on a region whose power system is controlled by an operator that relies on a regulation service to balance the total system supply to the total system load in quasi real-time. We consider the existing contractual framework in which a regulation unit declares its regulation parameters at the beginning of the contract, the operator guarantees that the regulation signals will be within the range of these parameters, and the regulation unit is rewarded proportionally to what it declares. Our purpose is twofold. We first want to obtain formulas for the regulation parameters that a unit with non-ideal storageshould declare to the operator given its state of charge at the beginning of a contract. Second, we want to analytically quantify, ahead of time, the reward that such a unit could obtain in successive contracts by performing this regulation service. Since the state of charge at the beginning of a contract depends on what happened in the previous contract and, hence, is a random variable, we quantify this reward analytically using bounds and expectation. We then provide engineering insights by applying our results to three specific energy storage technologies that are often considered as candidates for regulation. In particular, we show the impact of the storage parameters and the length of one contract on the potential reward over a given period.
- D. Fooladivanda, C. Rosenberg, and S. Garg, Analysis of Energy- and SoC-Neutral Contracts for Frequency Regulation with Energy Storage, Proc. IEEE Smart Grid Communications, November 2015.
Using storage to reduce diesel generator carbon footprint
Although modern society is critically reliant on power grids, modern power grids are subject tounavoidable 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 theuse 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 addressedusing 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.
- S. Singla, Y. Ghiassi-Farrokhfal, and S. Keshav. Using Storage to Minimize Carbon Footprint of Diesel Generators for Unreliable Grids, IEEE Transactions on Sustainable Energy, Vol 5, No. 4, pp. 1270-1277. 2014.
- S. Singla, Y. Ghiassi-Farrokhfal, and S. Keshav. Battery Provisioning and Scheduling For a Hybrid Battery-Diesel Generator System, ACM SIGMETRICS Performance Evaluation Review, December 2013.
Realistic EROI analysis
Large renewable energy (RE) farms, such as wind or solar farms are usually sited in remote areas, far from the transmission grid which typically interconnects population centers. Thus, they need to be connected on expensive access lines (distribution feeders) with limited capacity. The excess of RE generation over line capacity is wasted; this is called curtailment. We study curtailment using the metric of energy return on investment (EROI), defined as the ratio of useful energy extracted from each unit of energy invested in creating the renewable energy generation system. Curtailment reduces EROI. It may appear that we can extract more energy from an RE farm and increase EROI by adding storage to the system, where this storage is charged during generation peaks and discharged during off-peak times. However, manufacturing the storage requires an energy investment, and, after a certain number of cycles of usage, the storage becomes non-functional. Thus, adding storage may actually decrease the EROI. In this work, we study the EROI for RE farms when used with several types of storage technologies. Unlike prior work that makes numerous simplifying assumptions, our work accounts for storage size and storage imperfections and uses actual traces of renewable power generation. We find that lithium-ion batteries increase the EROI of both wind and solar farms, unlike lead-acid batteries which generally decrease their EROI. We also show that increasing access line capacity to achieve a target EROI is much more expensive for solar farms than for wind farms.
- Y. Ghiassi-Farrokhfal, S. Keshav and C. Rosenberg. An EROI-Based Analysis of Renewable Energy Farms with Storage, Proc. ACM e-Energy, June 2014.
Firming Solar Power
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.
- Y. Ghiassi-Farrokhfal, S. Keshav, and C. Rosenberg. Firming Solar Power, Extended Abstract/Poster, Proc. ACM SIGMETRICS, June 2013.
Impact of residential storage
It is anticipated that energy storage will be incorporated into the distribution network component of the future smart grid to allow desirable features such as distributed generation integration and reduction in the peak demand. There is, therefore, an urgent need to understand the impact of storage on distribution system planning. In this paper, we focus on the effect of storage on the loading of neighbourhood pole-top transformers. We apply a probabilistic sizing technique originally developed for sizing buffers and communication links in telecommunications networks to jointly size storage and transformers in the distribution network. This allows us to compute the potential gains from transformer upgrade de- ferral due to the addition of storage. We validate our results through numerical simulation using measurements of home load in a testbed of 20 homes and demonstrate that our guidelines allow local distribution companies to defer trans- former upgrades without reducing reliability.
The Ontario electrical grid is sized to meet peak electricity load. If this worst-case load were reduced, the government and Ontario tax-payers could defer large infrastructural costs, reducing the cost of generation and electricity prices. Storage, batteries that can store energy during times of low load and be discharged during times of peak load, is one proposed solution to reducing peak load. We evaluate the effect of storage on the electrical grid under different customer electricity pricing schemes. We find that for existing pricing schemes, adopting storage is not profitable. Furthermore, as the level of storage adoption in the population increases, pricing schemes that incentivize charging at times known to all homeowners will eventually increase the peak load rather than decrease it. However, in some circumstances particular levels of homeowner storage adoption helps the grid reduce peak load, and thus the grid may choose to subsidize the cost of storage. We discuss hypothetical pricing schemes under which storage adoption is profitable for homeowners.
- O. Ardakanian, C. Rosenberg, and S. Keshav. On the Impact of Storage in Residential Power Distribution Systems, ACM SIGMETRICS Performance Evaluation Review 40.3 (2012): 43-47.
- T. Carpenter, S. Singla, P. Azimzadeh, and S. Keshav. The Impact of Electricity Pricing Schemes on Storage Integration In Ontario, Proc. ACM e-Energy, May 2012.