arXiv subjects

Md Umar Hashmi

Publications and source records attributed to Md Umar Hashmi.

32 records · Page 2Linked to original sources

arXiv2022

Optimal Operation of HVDC Interconnector: Irish Case

In September 2018 EirGrid launched the new electricity market. These new market arrangements integrate the all island electricity market with European electricity markets, making optimal use of cross border transmission assets. Ireland operates three operational HVDC interconnectors: Moyle, East-West, and Greenlink and one in development Celtic interconnector which connect Ireland to Scotland, Wales, and France respectively. Irish market operator, EirGrid, can maximize their operational profit by using the price difference in these electricity markets. We propose a profit maximization modelling which considers the line losses and price difference in these different electricity markets and identifies the optimal import/export of power using HVDC interconnectors. These models in future should incorporate the distribution losses, renewable energy curtailment, and Irish power network congestion levels. The proposed modeling is the first step towards implementing a multi-objective HVDC interconnector operating strategy.

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arXiv2022

Chance constrained day-ahead robust flexibility needs assessment for low voltage distribution network

For market-based procurement of low voltage (LV) flexibility, DSOs identify the amount of flexibility needed for resolving probable distribution network (DN) voltage and thermal congestion. A framework is required to avoid over or under procurement of flexibility in the presence of uncertainty. To this end, we propose a scenario-based robust chance-constrained (CC) day-ahead flexibility needs assessment (FNA) framework. The CC level is analogous to the risk DSO is willing to take in flexibility planning. Multi-period optimal power flow is performed to calculate the amount of flexibility needed to avoid network issues. Flexibility is defined in terms of nodal power ramp-up and ramp-down and cumulative energy needs over a full day for each node. Future uncertainties are considered as multiple scenarios generated using multivariate Gaussian distribution and Cholesky decomposition. These scenarios are utilized to solve the flexibility needs assessment optimal power flow (FNA-OPF) problem. Zonal clustering of an LV feeder is performed using electrical distance as a measure and spatial partitioning. The FNA tool calculates ramp-up and ramp-down flexibility's power and energy requirements. Energy and power needs are often valued differently in many energy markets. We identify the marginal value of flexibility associated with energy and power needs separately. From numerical results for an LV feeder, it is observed that zonal flexibility needs assessment is more immune to uncertainty than nodal flexibility needs, making it more useful for DSOs to evaluate day-ahead flexibility procurement. We also propose a Pareto optimal mechanism for selecting CC level to reduce flexibility needs while reducing DN congestion.

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arXiv2022

Flexible and curtailable resource activation in three-phase unbalanced distribution networks

The need for flexibility and curtailable resources is crucial for ensuring the healthy operation of future distribution networks (DN). In this work, we propose a network-state driven framework that distribution system operators (DSOs) can utilize for activating flexible and curtailable resources for alleviating network voltage and thermal issues, while accounting for network voltage and current imbalances. This approach assumes the availability of dynamic network state information and uses nodal sensitivities for calculating a flexibility activation signal (FAS). The signal design is motivated by volt-Var and volt-watt inverter control, and thus bounded. The FAS also considers network voltage and current imbalances and incentivizes activation of active and reactive power flexibilities for reducing imbalance in addition to mitigating voltage and thermal imbalances in a three-phase unbalanced distribution network. The FAS design resembles optimal power flow duals, often used as locational marginal prices. The gains associated with the imbalance component of the objective function of three-phase unbalanced resource activation (TPU-RA) is performed using Pareto optimality. A numerical case study is presented showing the efficacy of the proposed framework in avoiding network issues while reducing voltage unbalance factor by more than 80\%. Further, DN's flexibility needs are quantified for location and time of day.

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arXiv2022

WEcharge: democratizing EV charging infrastructure

The sustainable growth of EVs will have to be met with proportional growth in EV charging infrastructure. With limited urban spaces to place new charging stations, shrinking profitability, privately owned charging facilities need to be shared. WEcharge will allow privately owned charging infrastructure to be shared with public EV owners using a business model. We propose a resource matching algorithm that takes into account incoming EV preferences, hard constraints for such EV, and provides the best suited resource for charging. We demonstrate the applicability of the matching model by showing a realistic case study with a Nissan Leaf 40 kW EV and 25 company and publicly owned charging stations (DC fast charger, AC rapid charger, level 1 and level 2 charger) in Hasselt, Belgium. The case study shows that consumer preferences will govern resource matching.

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arXiv2022

Evaluation of voltage magnitude based unbalance metric for low voltage distribution networks

Voltage unbalance in distribution networks (DN) is expected to grow with increasing penetration of single-phase distributed generation and single-phase loads such as electric vehicle chargers. Unbalance mitigation will be a significant concern as voltage unbalance leads to increased losses, reduced motor and inverter efficiency, and becomes a limiting factor for DN operation. The true definition of the unbalance metric needs phasor measurements of network voltage and current. However, such phasor measurements are generally not available in real life and as such approximate definitions are widely used due to their simplicity. This work aims to compare the true voltage unbalance definition and approximate unbalance metrics derived from phase voltage magnitude, as phase voltage magnitudes are commonly measured by digital metering infrastructure. For the comparison, multi-period power flow simulations are performed for 161 Spanish distribution feeders with R/X ratios varying from 2.87 to 14.68. We observe that phase magnitude-based unbalance metrics reasonably approximate the true unbalance for higher R/X ratios with a varying load power factor in a DN. Furthermore, the approximate unbalance metrics slightly improve for a low DN power factor due to the increase in DN unbalance. Therefore, the phase magnitude-based unbalance metric can be utilized for approximating DN unbalance.

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arXiv2021

Impact of subsidy on profitability of residential photovoltaics, battery and inverter in Flanders

The sustained future growth of renewable energy in the distribution network is governed by its financial viability. The recent subsidies in Flanders for photovoltaic (PV) and battery are used to calculate the payback period on prosumer investment in residential PV, battery and inverter installations. Realistic scenario of PV, battery and inverter are created based on the market catalogue of one manufacturer per product. Using these scenarios, one-year simulations for a residential prosumer are conducted in a rolling horizon. Luminus, an energy service provider in Limburg, time-of-use and feed-in-tariff contract rates are utilized. The numerical evaluation indicates that the policies prefer new PV installations. The subsidy comparison for 2021 to 2024 is also conducted. Oversizing PV compared to inverter size increases profitability. Batteries performing only arbitrage may not attract much investment, as the payback period is high. This shows the need to maximize the value addition of prosumer investment on residential batteries by participating in multiple goals.

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arXiv2020

Energy Storage Optimization for Grid Reliability

Large scale renewable energy integration is being planned for multiple power grids around the world. To achieve secure and stable grid operations, additional resources/reserves are needed to mitigate the inherent intermittency of renewable energy sources (RES). In this paper, we present formulations to understand the effect of fast storage reserves in improving grid reliability under different cost functions. Our formulations and solution schemes not only aim to minimize imbalance but also maintain state-of-charge (SoC) of storage. In particular, we show that accounting for system response due to inertia and local governor response enables a more realistic quantification of storage requirements for damping net load fluctuations. The storage requirement is significantly lower than values determined when such traditional response are not accounted for. We demonstrate the performance of our designed policies through studies using real data from the Elia TSO in Belgium and BPA agency in the USA. The numerical results enable us to benchmark the marginal effect on reliability due to increasing storage size under different system responses and associated cost functions.

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arXiv2020

Towards Phase Balancing using Energy Storage

Ad-hoc growth of single-phase-connected distributed energy resources, such as solar generation and electric vehicles, can lead to network unbalance with negative consequences on the quality and efficiency of electricity supply. Case-studies are presented for a substation in Madeira, Portugal and an EV charging facility in Pasadena, California. These case studies show that phase imbalance can happen due to a large amount of distributed generation (DG) and electric vehicle (EV) integration. We conducted stylized load-flow analysis on a radial distribution network using an openDSS-based simulator to understand such negative effects of phase imbalance on neutral and phase conductor losses, and in voltage drop/rise. We evaluate the integration of storage in the distribution network as a possible solution for mitigating effects caused by imbalance. We present control architectures of storage operation for phase balancing. Numerically we show that relatively small-sized storage (compared to unbalance magnitude) can significantly reduce network imbalance. We identify the end node of the feeder as the best location to install storage.

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arXiv2020

Energy storage applications for low voltage consumers in Uruguay

Energy storage can be used for many applications in the Smart Grid such as energy arbitrage, peak demand shaving, power factor correction, energy backup to name a few, and can play a major role at increasing the capacity of power networks to host renewable energy sources. Often, storage control algorithms will need to be \textit{tailored} according to power networks billing structure, reliability restrictions, and other local power networks norms. In this paper we explore residential energy storage applications in Uruguay, one of the global leaders in renewable energies, where new low-voltage consumer contracts were recently introduced. Based on these billing mechanisms, we focus on energy arbitrage and reactive energy compensation with the aim of minimizing the cost of consumption of an end-user. Given that in the new contacts the buying and selling price of electricity are equal and that reactive power compensation is primarily governed by the installed converter, the storage operation is not sensitive to parameter uncertainties and, therefore, no lookahead is required for decision making. A threshold-based \textit{hierarchical} controller is proposed which decides on the optimal active energy for arbitrage and uses the remaining converter capacity for reactive power compensation, which is shown to increase end-user profit. Numerical results indicate that storage could be profitable, even considering battery degradation, under some but not all of the studied contracts. For the cases in which it is not, we propose the best-suited contract. Results presented here can be naturally applied whenever the tariff structure satisfies the hypothesis considered in this work.

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arXiv2020

Storage Optimal Control under Net Metering Policies

Electricity prices and the end user net load vary with time. Electricity consumers equipped with energy storage devices can perform energy arbitrage, i.e., buy when energy is cheap or when there is a deficit of energy, and sell it when it is expensive or in excess, taking into account future variations in price and net load. Net metering policies indicate that many of the utilities apply a {customer selling} rate lower than or equal to the retail {customer buying rate} in order to compensate excess energy generated by end users. In this paper, we formulate the optimal control problem for an end user energy storage device in presence of net metering. We propose a computationally efficient algorithm, with worst case run time complexity of quadratic in terms of number of samples in lookahead horizon, that computes the optimal energy ramping rates in a time horizon. The proposed algorithm exploits the problem's piecewise linear structure and convexity properties for the \textit{discretization} of optimal Lagrange multipliers. The solution has a \textit{threshold-based structure} in which optimal control decisions are independent of past or future price as well as of net load values beyond a certain time horizon, defined as a \textit{sub-horizon}. Numerical results show the effectiveness of the proposed model and algorithm. Furthermore, we investigate the impact of forecasting errors on the proposed technique. We consider an Auto-Regressive Moving Average (ARMA) based forecasting of net load together with the Model Predictive Control (MPC). We numerically show that adaptive forecasting and MPC significantly mitigate the effects of forecast error on energy arbitrage gains.

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arXiv2020

Arbitrage with Power Factor Correction using Energy Storage

The importance of reactive power compensation for power factor (PF) correction will significantly increase with the large-scale integration of distributed generation interfaced via inverters producing only active power. In this work, we focus on co-optimizing energy storage for performing energy arbitrage as well as local power factor correction. The joint optimization problem is non-convex, but can be solved efficiently using a McCormick relaxation along with penalty-based schemes. Using numerical simulations on real data and realistic storage profiles, we show that energy storage can correct PF locally without reducing arbitrage profit. It is observed that active and reactive power control is largely decoupled in nature for performing arbitrage and PF correction (PFC). Furthermore, we consider a real-time implementation of the problem with uncertain load, renewable and pricing profiles. We develop a model predictive control based storage control policy using auto-regressive forecast for the uncertainty. We observe that PFC is primarily governed by the size of the converter and therefore, look-ahead in time in the online setting does not affect PFC noticeably. However, arbitrage profit are more sensitive to uncertainty for batteries with faster ramp rates compared to slow ramping batteries.

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arXiv2019

Sizing and Profitability of Energy Storage for Prosumers in Madeira, Portugal

This paper proposes a framework to select the best-suited battery for co-optimizing for peak demand shaving, energy arbitrage and increase self-sufficiency in the context of power network in Madeira, Portugal. Feed-in-tariff for electricity network in Madeira is zero, which implies consumers with excess production should locally consume the excess generation rather than wasting it. Further, the power network {operator} applies a peak power contract for consumers which imposes an upper bound on the peak power seen by the power grid interfaced by energy meter. We investigate the value of storage in Madeira, using four different types of prosumers, categorized based on the relationship between their inelastic load and renewable generation. We observe that the marginal increase in the value of storage deteriorates with increase in size and ramping capabilities. We propose the use of profit per cycle per unit of battery capacity and expected payback period as indices for selecting the best-suited storage parameters to ensure profitability. This mechanism takes into account the consumption and generation patterns, profit, storage degradation, and cycle and calendar life of the battery. We also propose the inclusion of a friction coefficient in the original co-optimization formulation to increase the value of storage by reducing the operational cycles and eliminate low returning transactions.

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arXiv2019

Energy Storage in Madeira, Portugal: Co-optimizing for Arbitrage, Self-Sufficiency, Peak Shaving and Energy Backup

Energy storage applications are explored from a prosumer (consumers with generation) perspective for the island of Madeira in Portugal. These applications could also be relevant to other power networks. We formulate a convex co-optimization problem for performing arbitrage under zero feed-in tariff, increasing self-sufficiency by increasing self-consumption of locally generated renewable energy, provide peak shaving and act as a backup power source during anticipated and scheduled power outages. Using real data from Madeira we perform short and long time-scale simulations in order to select end-user contract which maximizes their gains considering storage degradation based on operational cycles. We observe energy storage ramping capability decides peak shaving potential, fast ramping batteries can significantly reduce peak demand charge. The numerical experiment indicates that storage providing backup does not significantly reduce gains performing arbitrage and peak demand shaving. Furthermore, we also use AutoRegressive Moving Average (ARMA) forecasting along with Model Predictive Control (MPC) for real-time implementation of the proposed optimization problem in the presence of uncertainty.

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arXiv2019

Optimal Storage Arbitrage under Net Metering using Linear Programming

We formulate the optimal energy arbitrage problem for a piecewise linear cost function for energy storage devices using linear programming (LP). The LP formulation is based on the equivalent minimization of the epigraph. This formulation considers ramping and capacity constraints, charging and discharging efficiency losses of the storage, inelastic consumer load and local renewable generation in presence of net-metering which facilitates selling of energy to the grid and incentivizes consumers to install renewable generation and energy storage. We consider the case where the consumer loads, electricity prices, and renewable generations at different instances are uncertain. These uncertain quantities are predicted using an Auto-Regressive Moving Average (ARMA) model and used in a model predictive control (MPC) framework to obtain the arbitrage decision at each instance. In numerical results we present the sensitivity analysis of storage performing arbitrage with varying ramping batteries and different ratio of selling and buying price of electricity.

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