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Efficient management of energy resources is crucial in smart buildings. In this work, model predictive control (MPC) is used to minimize the economic costs of prosumers equipped with production units, energy storage systems, and electric vehicles. To this purpose, the predictive control manages the available energy resources by exploiting future in...

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... MPC algorithms have been used for years in process control; typical applications include chemical reactors [2], olefin metathesis processes [3], distillation towers [4] and power plants [5]. Nowadays, as a result of the availability of fast and relatively cheap hardware platforms necessary to carry out all online calculations, MPC algorithms are used in smart buildings [6] and several embedded systems; example applications include autonomous ground vehicle [7], autonomous driving vehicle [8], planning vehicle-parking trajectories for vertical parking spaces [9] and quadrotors [10,11]. Finally, MPC algorithms may control distributed parameter systems [12]. ...

... Next, we use Equation (6) to express the outputs of the second block of the model, which is the model output. The predicted model output signal iŝ ...

The Wiener model, composed of a linear dynamical block and a nonlinear static one connected in series, is frequently used for prediction in Model Predictive Control (MPC) algorithms. The parallel structure is an extension of the classical Wiener model; it is expected to offer better modeling accuracy and increase the MPC control quality. This work discusses the benefits of using the parallel Wiener model in MPC. It has three objectives. Firstly, it describes a fast MPC algorithm in which parallel Wiener models are used for online prediction. In the presented approach, sophisticated trajectory linearization is performed online, which leads to computationally fast quadratic optimization. The second objective of this work is to study the influence of the model structure on modeling accuracy. The well-known neutralization benchmark process is considered. It is shown that the parallel Wiener models in the open-loop mode generate significantly fewer errors than the classical structure. This work’s third objective is to validate the efficiency of parallel Wiener models in closed-loop MPC. For the neutralization process, it is demonstrated that parallel models demonstrate better control quality using various indicators, but the difference between the classical and parallel models is not significant.

... Mr. Nitin U Thakare1, Mr. Utkarsh Manwar2,et.al,(2020) according to the study a major effect of building on the total worldwide energy feasting level i.e. around 40% of the total energy is expended by only buildings and becoming a major main energy consumptive part of the global structure [13]. including energy management systems, renewable energy applications, and current advanced smart technologies for optimal function and energy-efficient performance [15]. ...

... A review of the literature on the subject suggests that there is currently no description of dynamic control for AC/DC converters in LV grids by application of simultaneous decentralised microcontrollers. As an alternative, the literature only indicates the use of BESS by the prosumer and the possibility of cooperation for the LV grid [41][42][43][44][45][46][47][48][49][50]. ...

The article is a continuation of the authors’ ongoing research related to power flow and voltage control in LV grids. It outlines how the Distribution System Operator (DSO) can use Machine Learning (ML) technology in a future grid. Based on supervised learning, a Selectively Coherent Model of Converter System Control for an LV grid (SCM_CSC) is proposed. This represents a fresh, new approach to combining off and on-line computing for DSOs, in line with the decarbonisation process. The main kernel of the model is a neural network developed from the initial prediction results generated by regression analysis. For selected PV system operation scenarios, the LV grid of the future dynamically controls the power flow using AC/DC converter circuits for Battery Energy Storage Systems (BESS). The objective function is to maintain the required voltage conditions for high PV generation in an LV grid line area and to minimise power flows to the MV grid. Based on the training and validation data prepared for artificial neural networks (ANN), a Mean Absolute Percentage Error (MAPE) of 0.15% BESS and 0.51–0.55% BESS 1 and BESS 2 were achieved, which represents a prediction error level of 170–300 VA in the specification of the BESS power control. The results are presented for the dynamic control of BESS 1 and BESS 2 using an ANN output and closed-loop PID control including a 2nd order filter. The research work represents a further step in the digital transformation of the energy sector.

... e economic costs for the MPC and the heuristic approach in the considered price scenarios are reported in the economic costs of prosumers equipped with production units, energy storage systems, and electric vehicles. To this purpose, the predictive control manages the available energy resources by exploiting future information about energy prices, absorption and production power profiles, and electric vehicle (EV) usage, such as times of departure and arrival and predicted energy consumption [14]. In the model predictive control and state feedback controller, the chosen states of the system are compared with their reference quantities to generate the converter switching. ...

,e distributed energy management of interconnected microgrids, which is based on model predictive control (MPC), relies on
the cooperation of all the agents (i.e., microgrids). Model predictive control or MPC is widely used in industrial applications as an
effective tool for dealing with multivariable limited control problems. MPC uses an explicit system model to predict the future
horizon of the system and its outputs. ,is predictability allows calculating the optimal order of inputs to minimize output errors
over a limited horizon, which is effected by the limitations of the system. ,is study presents a distributed economic model
predictive control method using the new state feedback controller to control the switching of interface converters and compensate
for the unbalanced and nonlinear loads. In this model, the islanding mode and the reconnection of the grid are considered to
improve the transient behavior of the system to achieve steady-state power distribution. It has been proposed that it could obtain
better results in predictive control, utilizing similarity transform in the state matrix and its modification. First, this model is
simulated on distributed generation sources with power-sharing and local loads using the state feedback controller in MATLAB
Simpower. ,en, the performance of the proposed method is evaluated, confirming that it is more reliable than the FS-MPC and
DSVM-MPC methods.

... In recent years, as the conventional resources are shrinking day-byday and simultaneously, the electric power demand is also increasing, so more emphasis is towards the integration of Renewable Energy (RE) sources [1]. The inclusion of RE, mainly the solar and wind power generation, is progressively growing over the last decade [2][3][4]. The splendid collective benefits of RE are no carbon emission, clean source, and environment friendly. ...

... where ω k = (k − 1/2) Δω, Φ k is the randomly varying term with uniform probability density within the limit of [0, 2 π], Δω = 0.5-2 rad/sec, σ [2] = 200 is the variance of V ns ,S V (ω k ) depict the spectral density function, and assuming n = 50. ...

... where η (transition efficiency of solar cell) = 0.1, S (area of SP array) = 4084 m [2],ϕ(irradiation input) in kW/m 2 and T a (ambient temperature) = 25 • C. A random step function is considered as incident solar insolation. P sp directly varies with inputϕ. ...

Demand response (DR) has emerged as a key component of the future electric power system’s reliability and
frequency stability. This study explores the effect of DR regulation and hybrid energy storage (HES) on an
identical two-area test power system that comprises of solar photovoltaic, wind turbine, biogas unit, and a
thermal power plant for improved frequency regulation services. By including intrinsic communication latency in
the DR loop, a generalised load frequency control (LFC) model of a hybrid hierarchical DR mechanism is
developed. The stability of the analysed system before and after DR is compared in terms of stability margin. A
Quasi- oppositional Harris Hawks Optimization (QOHHO) is suggested to optimize the coefficients of the proposed
cascade fractional order two-degree-of-freedom CC[FO(TDOF)] controller. The supremacy of the proposed
algorithm is affirmed by comparing its figure of demerit with different existing algorithms. The investigations
also manifest the efficacy of proposed controller over proportional integral derivative with filter (PID) and two
degree of freedom (tilt integral derivative with filter) [TDOF (TID)] controllers. QOHHO optimized designed
controller performance is also evaluated for the studied system under random load disturbances. Furthermore,
the suggested technique’s practical feasibility is verified through experimental validation using OPAL-RT based
real-time analysis.

... e economic costs for the MPC and the heuristic approach in the considered price scenarios are reported in the economic costs of prosumers equipped with production units, energy storage systems, and electric vehicles. To this purpose, the predictive control manages the available energy resources by exploiting future information about energy prices, absorption and production power profiles, and electric vehicle (EV) usage, such as times of departure and arrival and predicted energy consumption [14]. In the model predictive control and state feedback controller, the chosen states of the system are compared with their reference quantities to generate the converter switching. ...

The distributed energy management of interconnected microgrids, which is based on model predictive control (MPC), relies on the cooperation of all the agents (i.e., microgrids). Model predictive control or MPC is widely used in industrial applications as an effective tool for dealing with multivariable limited control problems. MPC uses an explicit system model to predict the future horizon of the system and its outputs. This predictability allows calculating the optimal order of inputs to minimize output errors over a limited horizon, which is effected by the limitations of the system. This study presents a distributed economic model predictive control method using the new state feedback controller to control the switching of interface converters and compensate for the unbalanced and nonlinear loads. In this model, the islanding mode and the reconnection of the grid are considered to improve the transient behavior of the system to achieve steady-state power distribution. It has been proposed that it could obtain better results in predictive control, utilizing similarity transform in the state matrix and its modification. First, this model is simulated on distributed generation sources with power-sharing and local loads using the state feedback controller in MATLAB Simpower. Then, the performance of the proposed method is evaluated, confirming that it is more reliable than the FS-MPC and DSVM-MPC methods.

... Many optimization algorithms can be used to overcome the shortcomings of PI controllers. For example, [28] uses the model predictive control (MPC) method to manage energy resources efficiently. The author of [29] designs an optimal model predictive controller for the nonlinear multi-area power system, while [30] uses MPC to manage the power in a hydrogen-based microgrid. ...

Regarding the microgrid with large-scale electric vehicle (EV) energy storage systems working at the vehicle-to-grid (V2G) mode, uncertain factors (e.g., the number of EVs feeding the microgrid shifts frequently) make the system unfixed, leading to the fact that it is difficult to precisely determine the real-time droop coefficients of the system, thereby degrading the performance of the traditional inverter control strategies that rely on the droop coefficients. To solve the problem, this paper proposes an errorless-control-targeted double control loop (DCL) technique based on robust MPC to control the microgrid with EV energy storage systems without using droop coefficients. Firstly, the structure of the DCL method is developed, with each component in the structure detailed. Compared to the traditional control strategies, the novel one regards the frequency, voltage, and currents as the control objectives instead of active/inactive power. It deserves to be mentioned that the frequency and voltage are regulated by proportional-integral controllers, while the currents are regulated by the finite control set model predictive control (FCS-MPC) method. Secondly, the impacts of system parameter uncertainties on the prediction accuracy of the FCS-MPC controller are analyzed clearly, illustrating that it is necessary to develop effective techniques to enhance the robustness of the controller. Thirdly, sliding mode observers (SMO) based on a novel hyperbolic function are constructed to detect the real-time disturbances, which can be used to generate voltage compensations by using automatic disturbance regulators. Then, the voltage compensations are adopted to establish a modified predicting plant model (PPM) used for the FCS-MPC controller. By using the proposed SMO-based disturbance detection and compensation techniques, the MPC controller gains a strong robustness against parameter uncertainties. Finally, a simulation is conducted on a microgrid system to verify the effectiveness of the proposed techniques, and the obtained results are compared with the traditional virtual synchronous machine (VSG) strategy relying on droop coefficients.

... In principle, each user may decide, time by time, which part of his capacity (stored energy, active and reactive power) can be shared with the rest of the E-LAN to improve local performance (e.g., voltage stabilization, loss reduction, power sharing, stress reduction) or to meet system-level requirements (e.g., demand response, power factor at the point of common coupling with utility, transition from on-grid to off-grid operation, fault clearing, etc.). The remaining capacity can be used for private needs, e.g., smoothing of domestic power absorption or trading energy in the market [2,8]. ...

Local area energy networks (E-LANs) are cyber-physical systems whose physical layer is a meshed low-voltage microgrid fed by a multiplicity of sources, i.e., utilities, energy storage systems, and distributed power sources. The cyber layer includes distributed measurement, control, and communication units, located at end-user premises, as well as centralized supervision and dispatchment control. As compared with standard microgrid, the E-LAN encompasses the ability for end-users to actively contribute to the operation of the microgrid while acting as independent energy traders in the electrical market. Operational goals include active contribution of end-users to power sharing, loss reduction, voltage stability, demand response, fault identification and clearing, isolation of sub-grids for maintenance, islanding, and black start. Economic goals include the possibility, for each end-user, to decide in every moment, based on convenience, how his energy and power capacity is shared with other users, e.g., for demand response or to trade energy in the electric market. This paper introduces a comprehensive theoretical approach of E-LAN control to achieve all the above operational goals while providing a high level of dynamic protection against faults or other events affecting the system functionality, e.g., overloads or fast transients. It shows that meshed microgrids are the necessary infrastructure to implement the desired functionalities.

... In the article by Simmini et al. [2], a model predictive control (MPC)-based technique is proposed for the efficient management of a home nanogrid equipped with a PV source, an ESS, electric vehicles (EVs), and a connection to the upstream power grid. By exploiting predictions of generation profiles, energy prices, times of departure and arrival of the EV, and daily EV battery energy consumption, the predictive control effectively manages the prosumers' energy resources. ...

Energy management systems (EMSs) have been introduced in electrical power systems to optimize operations of the electrical grid infrastructure and to provide support to the grid operator in terms of optimized decisions [...]