Demand Response Coupled with Dynamic Thermal Rating for Increased Transformer Reserve and Lifetime

(1) Background: This paper proposes a strategy coupling Demand Response Program with Dynamic Thermal Rating to ensure a transformer reserve for the load connection. This solution is an alternative to expensive grid reinforcements. (2) Methods: The proposed methodology firstly considers the N-1 mode under strict assumptions on load and ambient temperature and then identifies critical periods of the year when transformer constraints are violated. For each critical period, the integrated management/sizing problem is solved in YALMIP to find the minimal Demand Response needed to ensure a load connection. However, due to the nonlinear thermal model of transformers, the optimization problem becomes intractable at long periods. To overcome this problem, a validated piece-wise linearization is applied here. (3) Results: It is possible to increase reserve margins significantly compared to conventional approaches. These high reserve margins could be achieved for relatively small Demand Response volumes. For instance, a reserve margin of 75% (of transformer nominal rating) can be ensured if only 1% of the annual energy is curtailed. Moreover, the maximal amplitude of Demand Response (in kW) should be activated only 2–3 h during a year. (4) Conclusions: Improvements for combining Demand Response with Dynamic Thermal Rating are suggested. Results could be used to develop consumer connection agreements with variable network acces

Optimized Time Reduction Models Applied to Power and Energy Systems Planning -Comparison with Existing Methods

International audienceThe paper proposes a strategy for the time horizon reduction in power and energy studies. The method denoted Optimized Weighted Time Slices is compared with conventional approaches based on representative days that rely on unsupervised and supervised clustering as well as different strategies to reconstruct the problems. Those reference methods suffer from a lack of scalability when high numbers of dimensions are considered and their outputs strongly depend on the starting point in the partitioning process. The proposed strategy is based on a hierarchical clustering coupled with a least square minimization. The originality of the approach is that it works on individual time slices rather than on representative periods. Those representative time samples are furtherly optimized considering fitting criteria with the input time series thanks to a linearization of the duration curves. All the time modelling methods are tested on both a simple energy hub at a building scale (i.e. load, solar storage) and on a 33buses distribution network with storage. The methods performances are assessed while comparing the results of the systems operation over the reduced time horizons with the outputs from full yearly simulations. In particular, complex objective functions are considered for the systems operation, as it is shown that they impact the accuracy of the time reduction as much as the systems complexity itself. The proposed strategy displays smaller errors (1 %-5 % more accuracy) than the reference methods, is much more scalable (> 10 times faster), and systematically returns the same outputs. HIGHLIGHTS A method for dimensionality reduction of time series based on the optimization of representative samples. Application to power and energy systems management or design problems. Comparison with supervised and unsupervised daily profiles clustering. Impact study of the management objective functions and problem reconstruction strategies after time reduction. Higher accuracy for nonlinear objective functions, faster computation and replicability

Optimal design coupled with a management strategy for a microgrid with storage

L’augmentation de la consommation pour soutenir la croissance, le souci de réduction des gaz à effet de serre et les avancées technologiques ont favorisé le développement des sources d’énergie renouvelables depuis les années 90. L’implantation de ces générateurs décentralisés a progressivement modifié l’architecture du réseau en passant d’un modèle vertical à une situation davantage clusterisée. Ce réseau maillé voit ainsi apparaitre de nouveaux acteurs, à la fois producteurs et consommateurs (en anglais, les « prosumers»). Pour ce type de structures, la stratégie classique actuelle consiste à acheter l'ensemble de l'énergie consommée alors que la totalité de la production est vendue séparément à des tarifs intéressants. Avec les progrès réalisés sur les différentes technologies de stockage, de nouveaux degrés de liberté apparaissent et des opérations plus intelligentes deviennent possibles. L’objet de l’étude est un microréseau comprenant un générateur photovoltaïque et un consommateur tertiaire associés à un moyen de stockage. Deux technologies sont envisagées avec des volants d’inertie dans un premier temps et une batterie électrochimique (Li-ion) par la suite. Les domaines d’étude relatifs à ce type de système sont la gestion énergétique par planification, la commande temps réel et le dimensionnement. Les travaux de cette thèse se concentrent d’abord sur la problématique de gestion par optimisation des flux d’énergie. Différents algorithmes sont ainsi utilisés et comparés pour planifier le fonctionnement du microréseau. L’objectif est de diminuer la facture énergétique en tenant compte des données de consommation et production mais également des politiques tarifaires en vigueur et d’éventuelles contraintes de fonctionnement imposées par le fournisseur d’énergie. Dans un second temps la problématique de dimensionnement est abordée avec une démarche de conception optimale intégrant la boucle de gestion dès la phase de design. Nous montrons plus particulièrement comment l’adéquation entre les méthodes d’optimisation utilisées et le modèle du microréseau employé peut permettre la réduction significative des temps de calcul. Une configuration optimale du microréseau, valable sur des horizons temporels longs intégrant les alternances saisonnières, peut finalement se dégager. Les travaux se concluent sur une phase d’analyse avec des dimensionnements établis pour différents contextes tarifaires. Le but est de dégager des domaines permettant de valoriser et justifier l’installation d’un moyen de stockage qui s’avère indispensable pour soutenir le développement des sources d’énergies renouvelables et assurer la transition énergétique.To face the increasing demand of electrical power in compliance with the liberalization of the electricity market and the need of reducing CO2 emissions, many distributed energy resources have emerged and especially the generation systems that utilize renewable energy sources. In the nearfuture, the grid could be described as an aggregation of several microgrids both consumer and producer. For those "prosumers", a classical strategy consists in selling all the highly subsidized production at important prices while all consumed energy is purchased. Smarter operations now become possible with developments of energy storage technologies and evolving prices policies. The microgrid considered in the thesis is composed of an industrial load and a photovoltaic generator associated to an energy storage. Two technologies are considered with high speed flywheels on one hand and a Li-ion electrochemical battery on the other. The common study referring to such systems allude to the optimal scheduling, the real-time management and the sizing methodology. Firstly in the thesis, the optimal power flow dispatching is performed using various algorithms. Those operations aim at reducing the electrical bill taking account of consumption and production forecasts as well as the different fares and possible constraints imposed by the power supplier. Then the design strategy is investigated. The approach consists in simultaneously integrating the energy management and the sizing of the system. We particularly underline the complexity of the resulting optimization problem and how it can be solved using suitable optimization methods in compliance with relevant models of the microgrid. We specifically show the reduction of the computational time allowing the microgrid simulation over long time durations in the optimization process in order to take seasonal variations into account. In the last part a cost analysis is performed, and different design are computed depending on the prices policies. The goal is to determine a financial context that would encourage the deployment of storage systems that are necessary to favor the development of intermittent renewable energy sources

An Iterative Linear DistFLow for Dynamic Optimization in Distributed Generation Planning Studies

International audienceThis paper implements an iterative linear DistFlow for the modelling of radial distribution grids. The method is intended for dynamic optimizations under gird constraints, and in the presence of distributed generation including storage units with time coupled constraints. It is demonstrated that traditional piecewise linearization for the losses estimation in conventional linear DistFlow can lead to significant errors. This is due in particular to the setting of static upper bounds for the active and reactive branch flows in the linearization process, which may differ greatly from the actual power. The proposed iterative approach addresses this shortcoming with successive runs of linear DistFlow and updates for the flows upper bounds, dynamically along the simulated horizon. The method is compared to conventional linear DistFlow as well as other relaxed formulations such as Second Order Conic Programming and Quadratic Programming. All the methods are discriminated with regard to a reference AC power flow, in terms of error for the voltages and losses profiles on different test systems. The proposed iterative procedure displays the lowest error for the line losses with five to forty times more accuracy than conventional linearized formulations. It also outperforms the Second Order Conic relaxation in terms of scalability with one month dynamic simulation (at 1h time step) run in 7 min with 30 distributed units on a 69-bus system. The approach is further validated with typical uses cases for the operation, the sizing, and the siting of distributed assets consisting of solar generators and storage units. Especially, the procedure is coupled with a genetic algorithm in order to test different system configurations on a 90-bus system. The solutions are discriminated in terms of number of assets, installed capacities, connection bus(es), installation costs, system losses and system self-sufficiency

A generic benchmark for power market analysis from generation mix to end-users

This paper presents a benchmark to model power systems market mechanisms from the generation mix to the end-users. The objective is to develop a platform that can help solving long-term planning problems. The operation described here is the joined day-ahead (DA) clearing for the wholesale market and the individual optimal scheduling of end-users equipped with distributed solar generation and storage. The implemented methods lie on linear programming whose fast computation allows to consider the decentralized control of an important number of end-users. Several scenarios are investigated with different retail prices and CO 2 mitigation policies as well as different sizes for the decentralized assets at the end-users' level.National Research Foundation (NRF)Accepted versionThe authors acknowledge the support by the Singapore National Research Foundation under its Campus for Research Excellence And Technological Enterprise (CREATE) programme and its Energy NIC grant (NRF Award No.:NRF-ENIC-SERTD-SMES-NTUJTCI3C-2016). This research was also supported by the Cambridge Centre for Advanced Research in Energy Efficiency in Singapore Ltd (CARES)

Parameter Tuning for LV Centralized and Distributed Voltage Control with High PV Production

International audienceThis paper presents a centralized and a distributed voltage control strategies for a low voltage (LV) grid-connected microgrid with high penetration of photovoltaic (PV) systems. Both strategies are formulated as an optimal power flow (OPF) problem to minimize line losses and PV curtailments. Second-Order Conic Programming (SOCP) relaxation is formulated to "convexify" the OPF problem. Alternating direction method of multipliers (ADMM) is implemented for the distributed voltage control strategy. Moreover, weighting parameters for both controllers are introduced to have more flexibility in the controller operating mode. A sensitivity analysis on those weighting parameters allows to identify different operating regions, with priority given to loss reduction or curtailment minimization. In particular, we identify an infeasible region in cases where the considered SOCP relaxation is not valid, which highlights the need for fine parameter tuning of the controllers. The effectiveness of both voltage control strategies is validated in a LV radial microgrid. Index Terms-alternating direction method of multiplier (ADMM), optimal power flow (OPF), photovoltaic (PV) system, second order cone relaxation, voltage contro

Impact of the Economic Environment Modelling for the Optimal Design of a Multi-Energy Microgrid

International audienceThe paper discusses the impact of the problem formulation for the optimal design of a multi-energy microgrid. The study shows that the energy rates affect the output results much more than any other parameter such as the cost of equipment or the assets efficiencies (ratio 1 to 100). The main focus of the paper is then the representation of the economic environment for such a planning problems. Five different modeling approaches are investigated with both deterministic and stochastic methods (scenario-based formulation), as well as constant or increasing rates along the planning horizon. The obtained results show great impacts on the installed capacities (variations from simple to triple for the gas engine rated power), while the objective function (i.e. system cost of ownership) displays small variations less than 5 % with the different sets of hypothesis. The papers then concludes on a recommendation to present the results in terms of optimal areas instead of a single global optimum for such problems. I. NOMENCLATURE Operating Variables : P t ge , M t ge gas engine power/fuel at time t (kW , kg/h) P pv solar generation at time t (kW) P t bat+ , P t bat− battery charge/discharge at time t (kW) P t gd grid power at time t (kW)-generator conv. P gdP grid peak power along the emulated period (kW) P t ch chiller plant electrical load at time t (kW) P t abch absorption chiller electrical load at time t (kW) Q t ch chiller plant cooling power at time t (RT) Q t abch absorption chiller cooling power at time t (RT) Q t ts+ , Q t ts− charging/discharging power of ts at time t (RT) SOC t bat , SOC t ts battery/th. storage state of charge at time t (%) Sizing Variables: P geR , P pvR gas engine/solar gen. rated powers (kW, kWp) E batR , E tsR battery/th. storage capacities (kWh-RTh) Q tsR thermal storage rated power (RT) Parameters: P t l , Q t l electrical/thermal load at time t (kW, RT) P t pvN normalized solar generation at time t (-) π t e ,π p ,π t e elect./peak/gas prices ($/kWh,$/kW, $/kg) α ge , β ge coefficients for gas engine operating cost η bat , η ts battery and thermal storage efficiencies (-) SOC bat , SOC bat ̅̅̅̅̅̅̅̅̅ battery min/max state of charge (%) SOC ts , SOC ts ̅̅̅̅̅̅̅ min/max state of charge of battery ts (%) SOC 0 bat , SOC 0 ts battery and thermal storage initial SOC (%) COP ch chiller plant coefficient of performance (-) COP abch absorption chiller coefficient of performance (-) β pwh abch absorption chiller power/heat ratio (RT/kW) β pcr abch absorption chiller cooling ratio (kW/RT) II. INTRODUCTION Multi energy systems have long been proved to enhance the integration of renewable energy sources by providing efficiency and flexibility with the interaction of different energy vectors [1]. The optimal design of so-called "multi-energy microgrids" has been extensively addressed in the literature. Such problems consist in finding the best capacities of the assets (solar generator, co-generation plant, electrical/thermal storages, etc) with a tradeoff between the capital expenditures and the operating cost estimated along the system lifetime [2]. Such decision processes are subject to a wide range of uncertainties that has to be taken into consideration in the design phase-uncertainties regarding the load level, the renewable generation, the energy prices, the investment costs as well as the models accuracy [3]. Considering those uncertainties may lead to prohibitive computational times is usually tackled with different approaches mixed with simplified linear models of the systems. Typical methods rely on robust optimization with the definition of best/worst case scenarios that represent the volatility of uncertain parameters [4]. Other methods introduce chance constraints while setting a tolerance on the probability to meet specific operating conditions (typically power balance) [5]. Finally, scenario-based stochastic optimization is the most widely used approach. It define sets of potential scenario (with uniform or normal distribution to represent the uncertainties). The objective function is then formulated as the sum of the optimal results for each scenario weighted by the corresponding probability of occurrence [6] [7]. It is important to note that the majority of those studies consider the uncertainties for the load profiles and renewable-based generation. This paper investigates the relevance of the uncertainties consideration on a generic benchmark and wit

Uncertainties Impact and Mitigation with an Adaptive Model-Based Voltage Controller

International audienceThis paper investigates the impact of three sources of uncertainties on a conventional model-based voltage controller, which are i) model, ii) load/generation forecast and iii) grid impedance/data uncertainties. Due to the common lack of grid data availability at the low voltage level, a specific attention is attached to the mitigation of grid impedance uncertainties by proposing a convex optimization-problem based on local measurements to tune the controller parameters. Furthermore, a voltage performance index (V PI) is introduced to measure the efficiency of the proposed adaptive controller. The proposed simulations highlight how different types and levels of uncertainties can impact the controller performances. Furthermore, the proposed mitigation strategy shows a significant improvement on the controller performance in terms of voltage profiles. The studies are tested on 11-bus radial distribution system

Uncertainties Impact and Mitigation with an Adaptive Model-Based Voltage Controller

International audienceThis paper investigates the impact of three sources of uncertainties on a conventional model-based voltage controller, which are i) model, ii) load/generation forecast and iii) grid impedance/data uncertainties. Due to the common lack of grid data availability at the low voltage level, a specific attention is attached to the mitigation of grid impedance uncertainties by proposing a convex optimization-problem based on local measurements to tune the controller parameters. Furthermore, a voltage performance index (V PI) is introduced to measure the efficiency of the proposed adaptive controller. The proposed simulations highlight how different types and levels of uncertainties can impact the controller performances. Furthermore, the proposed mitigation strategy shows a significant improvement on the controller performance in terms of voltage profiles. The studies are tested on 11-bus radial distribution system

Model-free Detection of Solar Generation in Distribution Grids Based on Minimal Exogenous Information

International audienceThis paper proposes a strategy for the resources management and power allocation in an energy community. Especially, the fairness of the benefit sharing is assessed thanks to a metric introduced as a monthly net energy price (in ce/kWh) from the viewpoint of each individual and computed as the individual bill over the consumed energy. The community management decouples the operational phase (i.e., power dispatch) and the settlement (i.e., monthly community billing). In particular, the investigated billing approach is based on an optimization process with an additional constraint to limit the gap between the maximum and minimum identified prices over all the community members. This study then provides a new method to better address individual's need in the community. The results show a narrow range of the individual energy price and 11.5 % collective bill reduction compared to a case where the members act individually