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Increased penetration of renewable resources and new loads have increased the uncertainty levels in low voltage distribution systems (LVDS). This requires considering LVDS planning as a stochastic problem. Low voltage photovoltaics (PV) hosting capacity (HC) calculation is such a planning problem. Traditionally, this is solved by using the iterative Monte Carlo method, which requires solving the power flow equations thousands of times. This paper proposes a chance-constrained optimization-based hosting capacity calculation technique, which eliminates the necessity of repetitive solving of power flow equations. General polynomial chaos expansion is used to translate the input uncertainties defined by probability density function to the hosting capacity of the network. Chance constraints are applied for the nodal voltages and thermal overloading. A case study for an actual LV feeder shows that the computational time of using the Monte Carlo based method is reduced from days to the order of seconds without any sampling, relaxation, or linearization. Furthermore, the stochastic HC calculated by the proposed Stochastic Optimal Power Flow (SOPF) method gives the upper bound of the distribution network HC which can also be identified using conventional methods.

For market-based procurement of low-voltage 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. Future uncertainties are considered as multiple scenarios, which are utilized to solve the flexibility needs assessment optimal power flow (FNA-OPF) problem. The FNA tool calculates the temporal and locational ramp-up and ramp-down flexibility needs of the DN. We also propose a Pareto optimal mechanism for selecting CC level to reduce flexibility needs while reducing DN congestion. Zonal clustering of a DN feeder is performed using electrical distance as a measure, with spatial partitioning and silhouette coefficient. Further, a zonal and nodal bid matching algorithm is proposed, where alternate flexibility bids in the same zone are identified in case nodal matching is not exact, compared to the calculated FNA. The proposed bid matching algorithm assists the system operator in bid selection by avoiding rerunning power flow evaluations. The zonal and nodal bid matching leads to more than 43% additional reduction of probable network incidents compared to naive nodal bid matching. Furthermore, we also calculate FNA for a real German DN with 646 nodes and 331 consumers.

Flexibility needs assessment (FNA) refers to the amount of flexibility DSO needs to plan or procure from the flexibility market to avoid probable DNI. The probable DNI are captured using modeling uncer- tainties using generated scenarios that emulate the different realizations which could happen. The scenario generation utilizes the nodal load and generation forecast along with historical forecast errors. Flexibility needs assessment optimal power flow (FNA-OPF) problem is solved for each of the scenarios. The robust FNA, considering the worst-case scenario, if used for flexibility procurement would lead to substantial over-procurement. In order to avoid this, a risk-based index, e.g. a chance constraint (CC), is introduced for procurement. Higher values of the chance constraint would project on to greater risk DSO might have to encounter by facing unresolved DNI. DNI in low voltage grids are often a local problem in which flexible resources in the proximity respond to avoid these incidents. To that end we cluster distribution grids onto connected zones, extend the local search for flexibility resources and propose a zonal bid selection framework that selects flexibility bids from neighborhoods to avoid DNI.

The mitigation of distribution network (DN) unbalance and the use of single-phase flexibility for congestion mitigation requires accurate phase connection information, which is often not available. For a large DN, the naïve phase identification proposed in the majority of the prior works using a single voltage reference, this does not scale well for a multi-feeder DN. We present a consensus algorithm-based phase identification mechanism which uses multiple three-phase reference points to improve the prediction of phases. Due to the absence of real measurements for a real-suburban German DN, the algorithms are developed and evaluated over synthetic data using a digital twin. To utilize strongly correlated measurements, the DN is clustered into zones. We observe those reference measurements located in the same zone as the single-phase consumer, with unknown phase connectivity, leads to accurate prediction of DN phases. Four consensus algorithms are developed and compared. Using numerical results, we recommend the most robust phase identification mechanism. In our evaluation, measurement error, and the impact of the neutral conductor are also assessed. We assume limited DN observability and apply our findings to a German DN without smart meters, but only less than 8% of nodes have measurement boxes along with single-phase consumers with a home energy management system. Voltage time series for 1 month (hourly sampled) is utilized. The numerical results indicate that for 1% accuracy class measurement, the phase connectivity of 308 out of 313 single-phase consumers in a German DN can be identified. Further, we also propose metrics quantifying the goodness of the phase identification. The phase identification framework based on consensus algorithms for DN zones is scalable for large DN and robust towards measurement errors as the estimation is not dependent on a single measurement point.

{A curtailable and flexible resource activation framework for solving distribution network (DN) voltage and thermal congestions is used to quantify three important aspects with respect to modelling low voltage networks.} This framework utilizes the network states in the absence of such flexible or curtailable resources as the input for calculating flexibility activation signal (FAS). The FAS has some similarities with optimal power flow duals {associated with power balance constraint}. FAS due to drooping design, {incentivize corrective flexibility activation} prior to any network limit violations. {The nonlinear resource dispatch optimal power flow (RDOPF) utilizes FAS for the activation of flexible and curtailable resources. Solving the OPF problem for a large system is computationally intensive, and second-order cone (SOC) relaxation is often applied in the literature.} {First,} we highlight the multi-objective nature of SOC relaxed RDOPF. A Pareto front tuning mechanism {is proposed for choosing loss penalty factor} while reducing the optimality gap of the SOC relaxed RDOPF. {Secondly, we} present a methodology for evaluating temporal and locational flexibility needs assessment of a DN, which DSO's can utilize for flexibility planning {in operational timescales and procurement in the flexibility market}. {Lastly, we} quantify the impact of reactive power flexibility for a DN with varying load power factors. {Numerical simulations indicate that the presence of reactive flexibility reduces the active power flexibility needs by 50\% for the test feeder with 0.8 aggregated load power factor.}

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.

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.

No ISSN and DOI ispartof: Proceedings of the Power Systems Computation Conference 2022 ispartof: Power Systems Computation Conference (PSCC) 2022 location:Porto, Portugal date:27 Jun - 1 Jul 2022 status: Published online

Background: Rectal cancer (RC) remains a leading cause of cancer-related mortality worldwide. Despite existing therapeutics such as neoadjuvant radiotherapy, 5-year survival outcomes remain low at ~60%. Rectal tumors are heterogeneous and underpinned by dysregulated cellular signaling. This study aimed to investigate the IL6/JAK/STAT3 pathway as a prognostic biomarker in RC and explore the therapeutic potential of JAK inhibitors using in vitro/ex vivo models. Methods: A retrospective RC cohort (n=206) was stained via immunohistochemistry for IL6/JAK/STAT3 pathway members. Staining intensity was quantified and scores were analyzed for association with survival outcomes and clinicopathological characteristics. In a prospective RC cohort, the effect of radiation on IL6/JAK/STAT3 signaling was investigated in serially collected pre- and post-treatment tumor biopsies at the transcriptomic and protein level. Drug screening of repurposed JAK inhibitors (alone and in combination with radiation) was performed in cell lines (DLD-1, HCT116, HT29, SW620), mouse organoids (AKPT and KPN) and patient-derived organoids (PDOs). Responses were measured using WST-1 assays, clonogenics, and immunofluorescent staining for markers of proliferation. Results: High tumoral expression of pSTAT3tyr705 was associated with reduced cancer-specific survival (CSS) in RC patients with stroma-rich tumors (HR=1.781, 95%CI; 1.419-2.236, p=0.001). Similarly, high tumoral expression of JAK2 in stroma-rich cases was associated with reduced CSS (HR=1.854, 95%CI; 1.287-2.671, p=0.001). A significant increase in IL6/JAK/STAT3 gene signatures was observed at 2- and 6- weeks post treatment induction compared to baseline in rectal biopsy specimens (n=3). In vitro, JAK inhibition with 10µM AZD1480 or Ruxolitinib significantly reduced viability of cell lines HCT116 (p=0.018) and SW620 (p=0.050). When AZD1480 was combined with 0-8Gy of radiation, colony formation was significantly reduced upon combination treatment compared to vehicle controls in a dose-dependent manner and linear quadratic modelling showed a synergistic effect of combined treatment in HT29 (p=0.004) and SW620 lines (p=0.014). AKPT, KPN and a subset of PDOs showed a significant reduction in cell viability, proliferation and increased apoptosis following JAK inhibition. Conclusion: This study represented a step towards establishing the role of JAK inhibition as a therapeutic approach for RC. Ongoing research aims to validate the synergistic effect of combining JAK inhibition with radiation. Citation Format: Kathryn A. Pennel, Phimmada Hathakarnkul, Sara S. Al-Badran, Umar Hashmi, Lily Hillson, Jean A. Quinn, Leia Jones, Colin W. Steele, Donald C. McMillan, James H. Park, Owen J. Sansom, Joanna Birch, Campbell S. Roxburgh. JAK inhibition as a therapeutic approach to enhance radiation response in rectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1080.

International audience; To mitigate issues related to the growth of variable smart loads and distributed generation, distribution system operators (DSO) now make it binding for prosumers with inverters to operate under pre-set rules. In particular, the maximum active and reactive power set points for prosumers are based on local voltage measurements to ensure that inverter output does not cause voltage violations. However, such actions, as observed in this work, restrict the range available for local energy management, with more adverse losses on arbitrage profits for prosumers located farther away from the substation. The goal of the paper is three-fold: (a) to develop an optimal local energy optimization algorithm for activation of load flexibility and inverter-interfaced solar PV and energy storage under time-varying electricity prices; (b) to quantify the locational impact on prosumer arbitrage gains due to inverter injection rules prevalent in different energy markets; (c) to propose a computationally efficient hybrid inverter control policy which provides voltage regulation while substantially reducing locational disparity. Using numerical simulations on three identical prosumers located at different parts of a radial feeder, we show that our control policy is able to minimize locational disparity in arbitrage gains between customers at the beginning and end of the feeder to 1.4%, while PV curtailment is reduced by 91.7% compared to the base case with restrictive volt-Var and volt-watt policy.

Traditionally, the distribution system operator (DSO) relied on a fit-and-forget network design. However, there will be a greater need to integrate flexible and curtailable resources to cope with growing distributed generation (DG) installations and new consumer loads such as electric vehicles. Distribution network (DN) vulnerabilities, voltage and thermal limit violations, often require resource activation in the vicinity. Consumers located close to the end of the feeder in a radial DN witness more over-voltages (due to DG injection) and under-voltages (due to additional load). The per-unit change in active or reactive power of a consumer at the end of the feeder causes a greater marginal impact, referred to as nodal sensitivity. This will cause a higher activation of flexible resources close to the vulnerable nodes. A flat flexible activation priority will not be fair for such flexible owners. We propose a new mechanism to activate flexibility based on voltage sensitivity, which considers nodal sensitivities. The proposed activation design is motivated by inverter voltage control norms and has similarities with optimal power flow duals often utilized as locational marginal prices. These dual variables are active only when some network constraints are violated, and do not provide correction prior to such a violation. The proposed flexibility activation design, due to its drooping characteristics, actively contributes to avoiding such network issues from happening. The resource activation optimization formulation proposed is non-convex. Second-order cone relaxations are used to convexify the proposed resource activation optimization problem. We use numerical evaluations to show the proposed formulations can be used for activation of load flexibilities and can be used for valuing and planning flexible and curtailable resources.

International audience; Large scale renewable energy source (RES) integration planned for multiple power grids around the world will require additional resources/reserves to achieve secure and stable grid operations to mitigate the inherent intermittency of 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 not only aim to minimize imbalance but also maintain state-of-charge (SoC) of storage. The proposed approaches rely on a macroscopic supply-demand model of the grid with total power output of energy storage as the control variable. 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. Simulation case studies are embedded in the paper by using datasets from the Elia TSO in Belgium and BPA in the USA. The numerical results benchmark the marginal effect on reliability due to increasing storage size under different system responses and associated cost functions. Further we observe myopic control of batteries proposed approximates deterministic control of batteries for faster time scale reserve operation.

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., 10 pages, 8 figures

This paper presents a new co-optimization formulation for energy storage for performing energy arbitrage and power factor correction (PFC) in the time scale of minutes to hours, along with peak demand shaving in the time scale of a month. While the optimization problem is non-convex, we present an efficient penalty based convex relaxation to solve it. Furthermore, we provide a mechanism to increase the storage operational life by tuning the cycles of operation using a friction coefficient. To demonstrate the effectiveness of energy storage performing multiple tasks simultaneously, we present a case study with real data for a time scale of several months. We are able to show that energy storage can realistically correct power factor without significant change in either arbitrage gains or peak demand charges. We demonstrate a real-time Model Predictive Control (MPC) based implementation of the proposed formulation with AutoRegressive forecasting of net-load and electricity price. Numerical results indicate that arbitrage gains and peak demand shaving are more sensitive to parameter uncertainty for faster ramping battery compared to slower ramping batteries. However, PFC gains are insensitive to forecast inaccuracies.

International audience; 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.

International audience; 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.

With the massive deployment of distributed energy resources, there has been an increase in the number of end consumers that own photovoltaic panels and storage systems. The optimal use of such storage when facing Time of Use (ToU) prices is directly related to the quality of the load and generation forecasts as well as the algorithm that controls the battery. The sensitivity of such control to different forecast techniques is studied in this paper. It is shown that good and bad forecasts can result in losses in, particularly bad days. Nevertheless, it is observed that performing Model Predictive Control (MPC) with a simple forecast that is representative of the pasts can be profitable under different price and battery scenarios. We observe that performing MPC at a faster sampling time with a receding optimization horizon makes arbitrage less sensitive to uncertainties in forecasting. We use real data from Pecan Street and ToU price levels with different buying and selling price for the numerical experiments.

The objective of this work is to analyze the effect of real time electricity price (RTP) on the amount of ancillary services required for load balancing in presence of responsive users, information asymmetry and forecast errors in demand and renewable energy sources (RES) generation. We consider a RTP that is determined by the forecasted generation and ramping cost. A community choice aggregator manages the load of all the consumers by setting the price. The consumer's objective is to minimize their overall cost of consumption. Ancillary services are called upon to balance the load in real time. With zero RES in the power network and a high degree of load flexibility, the proposed RTP flattens and the volatility in demand vanishes. However, in presence of RES the volatility in price and demand is reduced up to an extent and ancillary services are required for load balancing. The amount of ancillary services required increases with forecast errors. We also propose a real time algorithm that approximates the optimal consumer behavior under the complete information setting. Extensive numerical simulations are provided using real data from Pecan Street and Elia Belgium.

International audience; Batteries are an expensive form of energy storage, therefore, must be operated in an efficient manner. Battery life is often described a combination of cycle life and calendar life. In this work we propose a mechanism to limit the number of cycles of operation over a time horizon in a computationally efficient manner. We propose a modification in an optimal arbitrage algorithm proposed in our previous work [1] to efficiently control the number of cycles of operation of a battery. The cycles of operation have to be tuned based on price volatility to maximize the battery life and arbitrage gains. We propose a mechanism to distinguish arbitrage returns.

International audience; Inexpensive energy from the wind and the sun comes with unwanted volatility, such as ramps with the setting sun or a gust of wind. Controllable generators manage supply-demand balance of power today, but this is becoming increasingly costly with increasing penetration of renewable energy. It has been argued since the 1980s that consumers should be put in the loop: " demand response " will help to create needed supply-demand balance. However, consumers use power for a reason, and expect that the quality of service (QoS) they receive will lie within reasonable bounds. Moreover, the behavior of some consumers is unpredictable, while the grid operator requires predictable controllable resources to maintain reliability. The goal of this chapter is to describe an emerging science for demand dispatch that will create virtual energy storage from flexible loads. By design, the grid-level services from flexible loads will be as controllable and predictable as a generator or fleet of batteries. Strict bounds on QoS will be maintained in all cases. The potential economic impact of these new resources is enormous. California plans to spend billions of dollars on batteries that will provide only a small fraction of the balancing services that can be obtained using demand dispatch. The potential impact on society is enormous: a sustainable energy future is possible with the right mix of infrastructure and control systems.

Battery storage is increasingly important for grid-level services such as frequency regulation, load following, and peak-shaving. The management of a large number of batteries presents a control challenge: How can we solve the apparently combinatorial problem of coordinating a large number of batteries with discrete, and possibly slow rates of charge/discharge? The control solution must respect battery constraints, and ensure that the aggregate power output tracks the desired grid-level signal. A distributed stochastic control architecture is introduced as a potential solution. Extending prior research on distributed control of flexible loads, a randomized decision rule is defined for each battery of the same type. The power mode at each time-slot is a randomized function of the grid-signal and its internal state. The randomized decision rule is designed to maximize idle time of each battery, and keep the state-of-charge near its optimal level, while ensuring that the aggregate power output can be continuously controlled by a grid operator or aggregator. Numerical results show excellent tracking, and low stress to individual batteries.

This paper proposes method for detection, estimation of Thevenin equivalent parameters to describe power system behavior. Thevenin equivalent estimation is a challenge due to variation in system states caused by power flow in the network. Thevenin equivalent calculation based on changes in system with multiple sources integrated with grid, isolated distributed generator system is analysed and nonlinear least square fit estimation technique for algorithm is adopted. Linear least square fit is used with a linearized model. Performance evaluation of proposed method is carried out through mathematical model, nonlinear and linear least square fit based algorithm technique and simulation through MATLAB/SIMULINK package. Accurate grid and source side impedance estimation technique is applicable for distributed generation sources interfaced with grid to improve dynamic response, stability, reliability when subjected to faults or any other disturbances in network. Algorithm can accurately estimate Thevenin equivalent of multiple sources connected in parallel simultaneously with voltage and current phasor measurements at point of common coupling. Mathematical analysis and simulation results validate the effectiveness of proposed method.

Accurate models for electric power load forecasting are essential for the operation and planning of power system from technical as well as financial perspective. Paper proposes an approach for short term electric load forecasting based on parameters which have been arrived from past load data using artificial neural network based Non linear autoregressive network exogenous technique. Novel approach for obtaining the seasonality factor, weekly trend and load increase pattern from past electricity consumption data are also proposed. Proposed methodology requires lesser real time inputs such as weather information. The real time active power load consumption data in MW for two and half years of Goa Electricity Board of Goa state from India is used for predicting future load demand. The results obtained from the model successfully predicts the future load data for week days with mean square error less than 1.67% and mean absolute deviation of 3.6%, which proves suitability of our proposed technique for forecasting.

Accurate metering and billing of actual energy consumed by consumers is integral to commercial management of an electric utility. An aggregate technical and commercial loss in India is around 27% and this is very high in spite of various reforms and measures by electricity boards across country. One of the significant components of these losses is Theft. Electricity theft is a grave problem across the globe. Improvement in aggregate technical and commercial losses in phased manner is very important for financial and technical viability of distribution companies in deregulated environment. The major challenge is to identify the location of the theft and estimate the amount of energy being stolen. Research to identify power pilferages and remedial measures to overcome them in electricity sector has been going on in recent years. The Paper proposes a conceptual approach to get both the approximate location and estimate of energy theft at that location. With both information available in real time, it will completely change the landscape of electrical sector across the nation.

Power electronic interfaced distributed generation sources are gaining importance due to numerous advantages such as reliability and efficiency. This paper proposes a novel control strategy based on virtual impedance design criteria and modification in conventional droop control to improve transient response, stability and power sharing for parallel operated inverters interfaced with Distributed generation sources. The control technique does not require any communication and automatically compensates for parameter variations in network. Mathematical analysis and simulation results are presented in the paper to prove the concept behind the control strategies.

International audience; 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 timescale 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 Predic-tive Control (MPC) for real-time implementation of the proposed optimization problem in the presence of uncertainty.

This paper presents a Techno-Economic assessment of the value proposition of introducing battery energy storage in the Madeira Island electric grid, where only micro-production for self-consumption is currently allowed. The evaluation was conducted against two local micro-producers using one year of energy consumption and solar PV production measurements. The assessments considered three different pairs of battery capacity/inverter size, and the outputs analyzed considering self-consumption, self-sufficiency, and energy costs. The results show that despite the increase in self-consumption and self-sufficiency, the value proposition of battery energy storage is still considerably low even considering a massive decrease in the costs of storage. Furthermore, the results also suggest that given the small size of the solar PV installations, inverters with half the size of the installed PV capacity represent the best value for money.

This paper presents a Techno-Economic assessment of the value proposition of introducing battery energy storage in the Madeira Island electric grid, where only micro-production for self-consumption is currently allowed. The evaluation was conducted against two local micro-producers using one year of energy consumption and solar PV production measurements. The assessments considered three different pairs of battery capacity/inverter size, and the outputs analyzed considering self-consumption, self-sufficiency, and energy costs. The results show that despite the increase in self-consumption and self-sufficiency, the value proposition of battery energy storage is still considerably low even considering a massive decrease in the costs of storage. Furthermore, the results also suggest that given the small size of the solar PV installations, inverters with half the size of the installed PV capacity represent the best value for money.