Day-ahead Operational Planning Research Articles

Data-Driven Distributionally Robust Optimization for Day-Ahead Operation Planning of a Smart Transformer-Based Meshed Hybrid AC/DC Microgrid Considering the Optimal Reactive Power Dispatch

Data-Driven Distributionally Robust Optimization for Day-Ahead Operation Planning of a Smart Transformer-Based Meshed Hybrid AC/DC Microgrid Considering the Optimal Reactive Power Dispatch

Day-Ahead Operational Planning for DisCos Based on Demand Response Flexibility and Volt/Var Control

Considering the integration of distributed energy resources (DER) such as distributed generation, demand response, and electric vehicles, day-ahead scheduling plays a significant role in the operation of active distribution systems. Therefore, this article proposes a comprehensive methodology for the short-term operational planning of a distribution company (DisCo), aiming to minimize the total daily operational cost. The proposed methodology integrates on-load tap changers, capacitor banks, and flexible loads participating in demand response (DR) to reduce losses and manage congestion and voltage violations, while considering the costs associated with the operation and use of controllable resources. Furthermore, to forecast PV output and load demand behind the meter at the MV/LV distribution transformer level, a short-term net load forecasting model using deep learning techniques has been incorporated. The proposed scheme is solved through an efficient two-stage strategy based on genetic algorithms and dynamic programming. Numerical results based on the modified IEEE 13-node distribution system and a typical 37-node Latin American system validate the effectiveness of the proposed methodology. The obtained results verify that, through the proposed methodology, the DisCo can effectively schedule its installations and DR to minimize the total operational cost while reducing losses and robustly managing voltage and congestion issues.

Multi-criteria dispatch optimization of a community energy network with desalination: Insights for trading off cost and security of supply

The idea of community energy network is being advocated to enhance the elasticity of diverse energy systems required for efficiently integrating a substantial volume of distributed energy resources. On the other hand, the interest in renewables-based desalination systems has received significant interest recently to consider freshwater as an additional end-use product in the community energy network system. Within this context, this paper introduces a multifaceted method for community energy networks with a focus on desalination-capable systems. The central goals involve diminishing the cumulative long-term expenses of the configuration, all while concurrently augmenting the system's capacity to store electrothermal energy on a daily basis that varies – all aimed at enhancing the reliability and security of resource provisioning. Importantly, the model co-optimizes the community energy network expenditure and reserve capacities, whilst integrating electrical, thermal, and natural gas vectors, as well as providing a platform for supplying freshwater needs. The overall freshwater provisioning infrastructure incorporates a water storage system, a desalination unit, a water well component, and a water pumping system. Furthermore, for the purpose of enhancing the adaptability, the community energy network concept put forth here utilizes coordinated electrothermal responsive load initiatives. These are coupled with meticulously planned electrothermal reservoir setups to curtail the wastage of surplus renewable production amidst diverse origins of unpredictability. The normalized weighted sum method is employed to convert the proposed formulation to a single-objective problem that is amenable to commercially available solvers in GAMS software. Then, the modelling framework is adapted to a system populated for a hypothetical site. The results verify the validity of the model in yielding globally optimum results for complex community energy networks with intertwined vectors of energy and end-use products. They also indicate that relatively small raises in the size of the electric and thermal reservoirs – and insubstantial raises in the expenditure of the system – can have potentially significant impacts on the ability of the system in serving loads during contingency conditions. In particular, by implementing demand response programs a cost reduction of 2.07% is shown, which is significant in the day-ahead operational planning phase.

An interval-based nested optimization framework for deriving flexibility from smart buildings and electric vehicle fleets in the TSO-DSO coordination

Emerging renewable-based transmission and distribution systems, despite many environmental and economic benefits, due to the intermittent nature of their production resources, compared to traditional systems, need more flexibility capacities, which necessitates the need for more suppliers of flexibility. To deal with these challenges, a nested framework is presented to derive the required flexibility of the transmission system operator (TSO) from distributed energy resources (DERs) and active end-users such as smart buildings (SBs) and electric vehicle (EV) fleets at the distribution level. To this end, a novel mechanism to design the demand response program (DRP) is introduced in which tariffs with time-varying rewards are built based on flexibility requirements. The coordination between TSO and distribution system operator (DSO) is initially modeled as a bi-level non-linear programming (NLP) problem, in which the upper-level is day-ahead (DA) operational planning of DS considering the schedules received from SBs, while the lower-level is DA operational planning of the TS. The bi-level NIL problem is transformed into a single-level linear programming (LP) problem by Krush Kuhn Tucker (KKT) conditions, Big-M method and Strong Duality Theory (SDT), which makes it computationally tractable. Finally, a two-stage interval-based algorithm solves the obtained single-level problem to secure the planning against uncertainties where battery energy storage systems (BESSs) are responsible for dealing with extreme conditions. The simulation results testify that the proposed interval-based nested framework has improved the economic, technical and security aspects of the TSO-DSO coordination since it has reduced the daily costs of the energy and flexibility markets, relieved lines congestion and improved voltage characteristics.

A Data-driven Robust Model for Day-ahead Operation Planning of Microgrids Considering Distributed Energy Resources and Demand Response

HIGHLIGHTS Novel data-driven approach to uncertainties in microgrid resources Faster convergence that stochastic optimization Reduced conservativeness compared to robust optimization Microgrid system with comprehensive selection of distributed energy resources

Attest project: tools for ancillary service procurement in day-ahead operation and real-time activation in distribution grids

The efforts to achieve carbon-neutral power systems aim to reduce the harmful effects of greenhouse gas emissions on climate. The energy systems of 2030 and beyond will face an increasing need for flexibility and generation back-up capacity in order to accommodate massive penetration of renewable energy sources (RES) together with the electrification of transport, heating and cooling sectors. In line with these changes, this paper describes two innovative tools developed in ATTEST project, that focus on the optimal procurement of ancillary services (AS) in day-ahead operation planning and optimal activation of AS in real-time operation of distribution grids. Specifically, these tools compute at the two different stages optimal settings of a variety of distributed energy resources namely: RES, battery storage units, shiftable (flexible) loads, particularly electric vehicles, and grid control means such as On-load Tap Changing (OLTC) transformers. The tools have been successfully tested on a variety of realistic models of distribution grids from UK, Croatia, Portugal and Spain.

Optimal Distributed Energy Resource Coordination: A Decomposition Method Based on Distribution Locational Marginal Costs

In this paper, we consider the day-ahead operational planning problem of a radial distribution network hosting Distributed Energy Resources (DERs) including rooftop solar and storage-like loads, such as electric vehicles. We present a novel decomposition method that is based on a centralized AC Optimal Power Flow (AC OPF) problem interacting iteratively with self-dispatching DER problems adapting to real and reactive power Distribution Locational Marginal Costs. We illustrate the applicability and tractability of the proposed method on an actual distribution feeder, while modeling the full complexity of spatiotemporal DER capabilities and preferences, and accounting for instances of non-exact AC OPF convex relaxations. We show that the proposed method achieves optimal Grid-DER coordination, by successively improving feasible AC OPF solutions, and discovers spatiotemporally varying marginal costs in distribution networks that are key to optimal DER scheduling by modeling losses, ampacity and voltage congestion, and, most importantly, dynamic asset degradation.

IMPACT OF TRANSFORMER AND CABLE AGING ON DISTRIBUTION LOCATIONAL MARGINAL COSTS IN ACTIVE DISTRIBUTION NETWORKS

This paper considers distribution network asset degradation, namely transformer and cable aging, in the context of the day-ahead operational planning in active distribution networks. The focus of this work is to quantify aging as a short-run network variable cost, and complement accordingly Distribution Locational Marginal Cost (DLMC) components. Short-run dynamic DLMCs are defined using sensitivity analysis of a centralized enhanced AC Optimal Power Flow model, providing incentives for optimal DER scheduling. Results indicate that optimal DER scheduling leads to smoother DLMC marginal aging component profiles compared to popular “open-loop”, e.g., Time of Use, pricing schemes.

Energy management of a renewable-based isolated micro-grid by optimal utilization of dump loads and plug-in electric vehicles

This paper proposes a new day-ahead energy management system (EMS) for an off-grid fishing island micro-grid (MG). The MG considered in this work is well equipped with smart grid infrastructure and embedded with plug-in electric vehicles (PEVs). Moreover, it is a green and fossil fuel free MG without any conventional power plant. The MG provides its load demand by solely renewable generations, including wind farms (WF) and photovoltaic (PV) power plants. Therefore, keeping balance between generation and demand is a difficult task in day-ahead operation planning of this MG. In order to overcome this obstacle, the MG uses a mega-scale battery energy storage system (BESS). However, the capacity of the BESS is limited and it is not economically feasible to increase the BESS capacity. Therefore, the MG considers PEVs G2V/V2G operation modes planning and dump loads optimal utilization as supplementary balance options. The main dump loads in this MG are industrial fish refrigerators (IFRs) and reverse osmosis desalination system (RODS). These dump loads in addition to PEVs’ G2V/V2G modes of operation are scheduled in the EMS proposed in this paper. Numerical studies show that the proposed EMS reduces total daily wasted energy and energy not served by 96% and 30% respectively.

Robustly coordinated operational scheduling of a grid‐connected seaport microgrid under uncertainties

The modern seaport system is becoming more electrified by integrating energy storage system, offshore renewable energy sources (RESs), and shore-to-ship power supply system. The energy management and operational scheduling aim to maximise the profit gain by transacting energy with the main grid, reduce the operation cost while minimising the emission at seaport territory. This work proposes a robustly coordinated operational scheduling method of a seaport microgrid as a grid-integrated energy hub under uncertain renewable energy sources power output and load demand. In the day-ahead operation planning, generators, unit commitment and shore-to-ship load schedules are optimised. To address the uncertainty in the renewable energy sources, the day-ahead operation planning is modelled as a two-stage robust optimisation model with conflicting objectives and solved with the column and constraints generation algorithm. These results are used as input parameters for the hour-ahead generation scheduling in the following day. With uncertainty realisation, a shorter time horizon with higher time resolution deterministic optimisation is carried out for the generation dispatch. The proposed method is verified with the IEEE-33 bus distribution network with different uncertainties sets. The simulation results have verified the robustness and effectiveness of the method in dealing with uncertainties. Coordination between day-ahead and hour-ahead not only improves the accuracy and performance of the final dispatch result but also meets the distribution network constraints at generation and load buses.

An advanced method for steady-state security assessment considering dynamic thermal capacities of grid assets

The ( $n-1$ ) criterion is a commonly used criterion for secure grid operation. Violations of this criterion lead to remedial actions in the short term and the construction of additional transmission capacities in the long term to reduce congestions in the grid. An alternative strategy in reducing necessary interventions is to consider the natural dependency of the transmission capacity of an installed grid component from its environmental conditions. The application of dynamic component rating is offering additional transmission capacities for system operation. For the consideration of these capabilities in the day-ahead operation planning, forecasting the trend of the component ratings is needed. The implementation of such methods, especially for overhead lines, is tested by different system operators around the world. A further strategy in enhancing transmission capacity for the operation is to utilize the thermal inertia of a component for small and short exceedances of the ratings without violating the component’s thermal limits. Currently available static emergency ratings of components respect this capability. Component ratings as well as emergency ratings are dependent on environmental conditions. Furthermore, the available emergency operation duration is dependent on the development of the loading current in the recent past. By equipping assets with real-time monitoring systems, utilization of the component’s current transmission capacity is possible for the different operation states. This paper presents a method to consider the thermal transmission capacity of power system components, enabled by real-time monitoring systems, in the contingency analysis. The approach is an assessment at system level combined with a time-domain simulation for the evaluation of the impact of an examined contingency on a component’s relevant thermal parameters. The result of the method is compatible with the current binary statement but extended with an availability duration if a contingency occurs in the examined grid. Introducing this duration can assist the planning of curative remedial actions for the operation. Further, three specific durations were proposed for benchmarking a contingency in the different levels of analysis. These durations are the result of the time-domain simulation. The presented method is capable of analysing a network consisting of assets equipped with a monitoring system and assets without such a system. The second part of this paper presents the operation simulation of an exemplary grid for one operational year to illustrate the effect of the approach on the results of the contingency analysis in day-ahead planning.

Operation planning and decision-making approaches for Volt/Var multi-objective optimization in power distribution systems

This paper presents a new specialized evolutionary system for Volt/Var optimization (SES-VVO) to efficiently solve the multi-objective optimization problem (MOP) when the number of tap-changing operations of voltage regulating devices is considered together with the conservation voltage reduction (CVR) goals. This problem is a challenging task for evolutionary algorithms (EAs) in terms of meeting voltage limits constraints and reaching near optimal solutions. The proposed mathematical model allowed the development of specialized search mechanisms, which contributed to improve the overall performance of the day-ahead operation planning and the hourly decision-making approaches, giving more realistic and cost saving solutions for distribution systems’ operators. Simulations on the IEEE 34-bus system were carried out in the presence of photovoltaic (PV) generation. Comparisons with other methods were conducted to clarify the effectiveness of the proposed method. The SES-VVO corroborated with the development of an advanced strategy that has potential for real-time operation considering the variation of load demand and the intermittence of PV generation, which were modelled by using Monte Carlo simulations. Finally, it was demonstrated that the tap change costs of voltage regulating devices have a decisive impact on the economics when the CVR goals are pursued by the SES-VVO approach proposed in this paper.

Distribution Network Marginal Costs: Enhanced AC OPF Including Transformer Degradation

This paper considers the day-ahead operational planning problem of radial distribution networks hosting Distributed Energy Resources, such as Solar Photovoltaic (PV) and Electric Vehicles (EVs). We present an enhanced AC Optimal Power Flow (OPF) model that estimates dynamic Distribution nodal Location Marginal Costs (DLMCs) encompassing transformer degradation as a short-run network variable cost. We decompose real/reactive power DLMCs into additive marginal cost components representing respectively the costs of real/reactive power transactions at the T\&D interface, real/reactive power marginal losses, voltage and ampacity congestion, and transformer degradation, capturing intertemporal coupling. Decomposition is useful in identifying sources of marginal costs and facilitating the employment of distributed AC OPF algorithms. DLMCs convey sufficient information to represent the benefit of shifting real/reactive power across time and achieve optimal Distribution Network and DER operation. We present and analyze actual distribution feeder based numerical results involving a wealth of future EV/PV adoption scenarios and illustrate the superiority of our approach relative to reasonable conventional scheduling alternatives. Overwhelming evidence from extensive numerical results supports the significant benefits of internalizing short-run marginal asset - primarily transformer - degradation.

Security-Constrained Day-Ahead Operational Planning for Flexible Hybrid AC/DC Distribution Networks

A new active network management framework is presented based on a multi-period optimal power flow problem that is bounded by security constraints at the distribution level for upholding the security of supply. This can be achieved through active engagement with flexible demand and distributed generation to prepare for contingency events in day-ahead operational planning. This framework is coupled with a flexible hybrid AC/DC medium voltage (MV) distribution network topology. It contains an integrated multi-terminal medium voltage DC (MVDC) interface for a seamless interaction and integration of the flexible demand and generation on both AC and DC sides of the hybrid network. The active energy management framework when coupled with a flexible hybrid AC/DC topology provides unprecedented degrees of flexibility as well as security of operation under a variety of conditions. To this end, the 75-bus UK generic distribution network has been modified and converted into a hybrid AC/DC network using the integrated MVDC interface. This framework is then deployed to minimise operational costs to the network operator, considering costs of schemes such as distributed generation curtailment and flexible demand shifting, as well as network losses. Results show a significant improvement in operational costs when the network operates as a flexible hybrid when compared to a pure AC or a more conventional AC/DC hybrid.

Day-Ahead Scheduling of a Local Energy Community: An Alternating Direction Method of Multipliers Approach

The paper focuses on the day-ahead operational planning of a grid-connected local energy community (LEC) consisting of an internal low-voltage network and several prosumers including generation units, battery storage systems, and local loads. In order to preserve, as much as possible, the confidentiality of the features of prosumers’ equipment and the production and load forecasts, the problem is addressed by designing a specific distributed procedure based on the alternating direction method of multipliers (ADMM). The distributed procedure calculates the scheduling of the available energy resources to limit the balancing action of the external grid and allocates the internal network losses to the various power transactions. Results obtained for various case studies are compared with those obtained by a centralized optimization approach. The results confirm that, in the considered LEC framework, each of the prosumers achieves a reduction in costs or increases revenues in case it participates to the LEC with respect to the case in which it can only transact with an external energy provider.

Robust Day-Ahead Operation Planning of Unbalanced Microgrids

The optimal energy management of microgrids in the face of uncertainty is a challenging task. It requires practical modeling of the microgrid along with an efficient decision-making tool to effectively improve the economical operation and power quality. This paper presents a robust optimization framework for the day-ahead operation planning of unbalanced three-phase microgrids. The operation planning problem is formulated in detail as a mixed-integer nonlinear programming model. This model includes the operational constraints pertaining to voltage unbalance, synchronous generator unbalance operation, and power unbalance at the point of common coupling. Linearization techniques are proposed to transform this problem into a mixed-integer linear programming model for which efficient solvers are available. Two-stage robust optimization is used to obtain a robust operation planning decision against realizations of uncertain renewable generation, real-time market price, and voltage-dependent load parameters. This problem is solved using the column and constraint generation algorithm. The IEEE 37-bus and a 60-bus feeders are used to validate the performance of the robust solution.

Energy Storage Scheduling in Distribution Systems Considering Wind and Photovoltaic Generation Uncertainties

Flexible distributed energy resources, such as energy storage systems (ESSs), are increasingly considered as means for mitigating challenges introduced by the integration of stochastic, variable distributed generation (DG). The optimal operation of a distribution system with ESS can be formulated as a multi-period optimal power flow (MPOPF) problem which involves scheduling of the charging/discharging of the ESS over an extended planning horizon, e.g., for day-ahead operational planning. Although such problems have been the subject of many works in recent years, these works very rarely consider uncertainties in DG, and almost never explicitly consider uncertainties beyond the current operational planning horizon. This article presents a framework of methods and models for accounting for uncertainties due to distributed wind and solar photovoltaic power generation beyond the planning horizon in an AC MPOPF model for distribution systems with ESS. The expected future value of energy stored at the end of the planning horizon is determined as a function of the stochastic DG resource variables and is explicitly included in the objective function. Results for a case study based on a real distribution system in Norway demonstrate the effectiveness of an operational strategy for ESS scheduling accounting for DG uncertainties. The case study compares the application of the framework to wind and solar power generation. Thus, this work also gives insight into how different approaches are appropriate for modeling DG uncertainty for these two forms of variable DG, due to their inherent differences in terms of variability and stochasticity.

A comprehensive framework for optimal day-ahead operational planning of self-healing smart distribution systems

Providing a cost-efficient and sustainable energy is one of the critical features in modern societies. In response to this demand, this paper proposes a comprehensive framework for optimal day-ahead operational planning of smart distribution systems considering both normal and emergency conditions. The proposed procedure for normal mode minimizes the operation costs and provides sustainability using the seamlessness index. By adjusting this index, the system can be adapted to achieve the desired self-sufficiency level along a specified planning horizon thanks to exploiting the sufficient local generations. The operational planning of emergency mode is integrated into the proposed framework to provide the optimal schemes which can handle the possible abnormal conditions using the available local generations and guarantee the desired resiliency level. In emergency mode, the proposed self-healing strategy will sectionalize the isolated area of the distribution system into island partitions to provide reliable power supply to the critical loads continuously. A set of key operational metrics including power loss, load priority, and system-related constraints are integrated into the proposed island partitioning procedure which promotes the functionality of the method. The proposed framework is implemented on a modified PG&E 69-bus distribution system and is investigated through different case studies. The results of case studies demonstrate significant improvements and benefits which are obtained by applying the proposed framework.

Multiple Perspective-Cuts Outer Approximation Method for Risk-Averse Operational Planning of Regional Energy Service Providers

In the smart grid and future energy internet environment, a regional energy service provider (RESP) may be able to integrate multiple energy resources such as generator units, demand response, electrical vehicle charging/swapping stations, and carbon emission trading to participate in the market. By imploring a well-known portfolio optimization theory conditional value-at-risk to tackle electricity price uncertainty, this paper formulates the risk-averse day-ahead operational planning for such a RESP as a mixed-integer quadratically constrained programming (MIQCP), named as RA-RESP. A global optimization method, named as multiple perspective-cuts outer approximation method (MPC-OAM) is proposed to solve this model efficiently. A remarkable stronger and tighter mixed integer linear programing master problem is designed to accelerate the convergence of the proposed method. Comprehensive simulation results show that, compared with existing day-ahead planning models, the RA-RESP is a good compromise between profit-based models and cost-based ones. The proposed MPC-OAM can solve complicated RA-RESP problem efficiently, and compared with state-of-the-art solution techniques, the MPC-OAM outperforms in both computing speed and solution quality, especially for scenario which includes more nonlinear factors.

Optimal operation planning of V2G-equipped Microgrid in the presence of EV aggregator

An optimal day-ahead operation planning procedure for Microgrids (MGs) integrating Electric Vehicles (EVs) in vehicle-to-grid (V2G) configuration is described in this work. It aims to determine the day-ahead operation plan by solving a non-linear optimization procedure involving daily cost and subject to dynamic operating constraints. The day-ahead operation plan aims to minimize MG operation daily costs, according to suitable load demand and source availability forecast, in the presence of an EV aggregator. In order to account for possible economic relationships between the EV aggregator and the MG operator, two different objective functions are considered. In order to investigate the influence of EV aggregator role on MG optimal operation management in different frameworks, the proposed approach is applied to a test MG taking into account residential or commercial customers’ load and EV exploitation profiles.