Multi-objective Control Strategy Research Articles

Multi-Objective Control Strategy for Switched Reluctance Machine with On-Line Efficiency Estimation and Improved On-Line Instantaneous Torque Estimation

Multi-Objective Control Strategy for Switched Reluctance Machine with On-Line Efficiency Estimation and Improved On-Line Instantaneous Torque Estimation

Multi-objective distributed control of WT-PV-BESS integrated weak grid via finite time containment

This paper proposes a finite time containment control scheme to achieve distributed control of a weak grid which is integrated with photovoltaic (PV), wind turbine (WT) and battery energy storage system (BESS). Taking advantage of the containment control in multi-dimensional control, we proposed a multi-objective control scheme of the WT-PV-BESS system in a weak grid, wherein four control objectives are specified, namely, bus voltage regulation, grid frequency control, peak load shaving and economic benefits optimization for the system operator. Firstly, the sizing approach of BESSs to fulfill the operation mission requirements is proposed; meanwhile, the general coordination strategy for the PV, WT and BESS to achieve the demand–supply balance is proposed. Subsequently, the predefined four control objectives are modeled and transferred into the long/short time frame operational constraints of the BESSs. Afterwards, the finite time containment control algorithm is proposed to force the BESSs into achieving containment status within prescribed time. Simulation case studies are conducted on a modified IEEE 9-bus system to show the effectiveness and performance of the proposed scheme.

Demand side flexibility for a Heat Booster Substation with ultra low temperature district heating

Ultra-low temperature district heating (ULTDH) systems improve the heat distribution efficiency by reducing thermal network losses and provide an easier access to Renewable Energy Sources (RES) by utilizing them as heat sources through Power-to-Heat solutions. In this study, Domestic Hot Water (DHW) preparation at a Heat Booster Substation (HBS) supplied with ULTDH is investigated for generating demand side flexibility. The main components of the HBS are a Hot Water Storage Tank (HWST) and an electric-driven heat pump. A genetic algorithm based control strategy is developed to optimally utilize the thermal energy storage capability of the HBS by intelligently scheduling the charging of the HWST based on time-varying electricity price signals. A multi-objective control strategy is designed to reduce energy costs while generating flexibility on the demand side. The components of the HBS are numerically modeled and experimentally validated to form a dynamic non-linear model of the HBS. Further, the data-driven seasonal ARIMA model, ARIMA(0,1,1)x(0,1,1)168, is developed to forecast the DHW consumption based on a statistical analysis of the historic consumption profiles over a two-year period. The forecast RMSE of the DHW prediction model is less than 0.03kgs−1 over the two-year test period, indicating that the prediction model is able to accurately forecast the DHW consumption of the residents. With the control-oriented HBS numerical model and the DHW prediction model serving as decision support tools, the developed control strategy is implemented at the HBS. Results show that in comparison to a rule-based control strategy, the developed control strategy demonstrates significant demand side flexibility while attaining energy cost savings of more than 91% and 84% at time resolutions of 15 and 30 min for the control interval respectively. With increased volatility in electrical prices due to higher penetration of RES, greater energy cost savings of more than 150% are observed.

Assessment of automated shading systems’ utilization and environmental performance: An experimental study

This paper presents an experimental study conducted in a full-scale test cell “btga-box” from July until September 2020 at Wuppertal University, Germany. This study focused on evaluating occupant interaction and satisfaction with automated shading systems in office environments, and the underlying thermal and visual conditions under different scenarios to optimize automated shading design and operation for improving occupant comfort and energy efficiency. Six different scenarios were evaluated using several performance metrics. Twenty-eight participants of varying ages, gender, and ethnicity took part in the experiments. After each scenario, the participants were asked to complete a web-based questionnaire to report their behaviour, their perceived thermal and visual comfort, satisfaction, and preferences concerning the performance of automated shading controls. Concurrently, indoor environmental parameters, weather data, and system and user-triggered actions were recorded. The key findings of this study suggest that a robust shading system (i.e., few override actions) can be achieved by: deploying a multi-objective control strategy with an intermediate position, an acceptable range of irradiance thresholds, and a decent level of other adaptive behavioural control options (i.e., window opening, fan usage, and light control). Providing active cooling or ventilation systems (i.e., operable windows, exhaust ventilation, and ceiling fan) can improve user satisfaction with the indoor environment, including shade operation. Using a small window size combined with a high irradiance shade-lowering threshold decreased the user-raising actions by 49% compared to the low threshold. However, energy implications should be considered for further studies since user satisfaction with blind control should be balanced with energy efficiency.

A Multi-objective Control Strategy of the Energy Storage System in Distribution Networks with a High Proportion of Renewable Energy

With the development of battery technology and power electronic technology, battery-based energy storage has been widely used in Peak Shaving and Valley Filling frequency and voltage regulation for the power system with a high proportion of renewable energy integration. However, the existing control strategies of the energy storage system (ESS) mostly take peak shaving and valley filling frequency and voltage regulation as a single control objective, causing the rest capacity of the energy storage in the single control working period to be wasted, which significantly reduces the utilization rate and economic benefits of energy storage. How to improve the utilization rate and economic benefits of the ESS while enhancing the support performance of ESS has not received much attention. This paper proposes a multi-objective control strategy of ESS to maximize energy storage’s benefit and ensure the distribution network’s safe and stable operation. First of all, this paper combines the auxiliary compensation benefits of the price system of distributed energy storage power stations and proposes a comprehensive index that comprehensively considers the economy of the ESS and the stable operation of the distribution network to support the evaluation needs of multi-objective control of energy storage. On this basis, this paper predetermines the multi-layer judgment logic for ESS operation control under different operating conditions, divides the ESS into different operating conditions according to the control objectives, and proposes a multi-objective control strategy based on variable droop coefficients. It effectively utilizes the idle capacity of the ESS and achieves an optimized control of energy storage that takes into account both technology and economy. The simulation verification based on the energy storage model built in Matlab/Simulink is conducted to validate the effectiveness of the proposed ESS multi-objective control strategy.

Multi-objective control strategy for multilevel converter based battery D-STATCOM with power quality improvement

Differences in battery state-of-charge (SOC) in a cascaded H-bridge multilevel converter (CHB-MLC) based distribution static synchronous compensator (D-STATCOM) in battery storage system (BSS) applications could result in undesirable efficiency and performance reductions, and even system failure. Moreover, faults occurring on batteries and/or H-bridges (modules) of CHB-MLC severely increase the risk of failure. Power quality at the output of CHB-MLC is reduced as results of those faults. Therefore, active power differences that are introduced by SOC balancing among modules and bypassing the faulty modules yield to undesired harmonics at the output of CHB-MLC. This paper proposes a multi-objective control strategy for CHB-MLC based BSS D-STATCOM. The proposed control strategy includes a bidirectional power flow controller with unity power factor operation (UPF) feature, a fault-tolerant (FT) controller, a SOC balancing scheme that is designed to work on fault conditions, and a harmonic minimization strategy to enhance the power quality at the output of the CHB-MLC. Performance of the proposed method is validated through different operating conditions including faults, unbalanced SOCs and dynamic load changes. Results indicate that charging and discharging of the batteries with 5 kW rated power are held successfully along with maintaining UPF operation under both normal and fault conditions. Moreover, grid currents are balanced with the help of the FT controller and SOC balancing is properly operated even in fault conditions. Negative impacts of faults and unbalanced operating conditions on the power quality are also mitigated with the help of the harmonic reduction controller with significant decreases on total harmonic distortion reaching up to 47% and 69% during normal operation and faulty operations, respectively.

Thermodynamic process control of ocean thermal energy conversion

The small temperature difference in ocean thermal energy conversion (OTEC) causes the high sensitivity of thermodynamic process to perturbations such as temperature and power load, which requires an efficient control strategy of thermodynamic process for OTEC. To this aim, an integral dynamic model is established to study the dynamic response of thermodynamic process in an OTEC system. To ensure the system efficiency and operating safety of power cycle, a multi-objective active-disturbance rejection control (ADRC) strategy is proposed and compared with corresponding single-objective ADRC and PI controller. Multi-objective ADRC includes a load-following control loop and two auxiliary control loops (evaporator control loop and condenser control loop). The results indicate that the multi-objective ADRC is most efficient to track the change of power load, in which the overshoot and integration absolute error (IAE) of load-following are reduced by 50% and 16% when compared with the single-objective ADRC. In addition, the multi-objective control strategy can maintain the stability of superheat degree and condensation pressure, and hence improves the system efficiency by 8.64% for the output power of 35 kW. Especially, the feedback-feedforward controller and feedback controller are attractive candidates to maintain the stable superheat degree and condensation pressure.

Multi-Objective Control Strategy for Switched Reluctance Generators in Small-Scale Wind Power Generations

Switched reluctance generators (SRGs) are widely used in wind power generation. However, due to the natural tendency of SRG, there are always nonnegligible conflicts to achieve high efficiency and low output voltage ripple at the same time. This creates difficulties for the high-performance of SRG. Thus, a multi-objective optimization control strategy is proposed in this paper to improve the static performance of SRG. The proposed control strategy contains following steps. First, in order to gain the maximum output power range at different rotor speeds, the turn-off angle is optimized off-line by simulated annealing algorithm (SAA). The optimized results are fitted as a function of rotor speed for on-line regulating; then, a closed-loop controller is built, and the reference current is regulated according to the difference between actual output power and required output power. Second, a multi-objective function is constructed as the evaluation result of SRG performance, which takes system efficiency, output voltage ripple and power converter loss into consideration. In the end, the turn-on angle is tuned by SAA according to the real-time multi-objective evaluation result. The proposed control strategy can be flexibly applied to SRGs with different structures and avoids the disadvantage of single-objective optimization. The simulation and experiments results show that the overall performance of SRG is improved.

A Multiobjective Control Strategy for Harmonic Current Mitigation With Enhanced LVRT Operation of a Grid-Tied PV System Without PLL Under Abnormal Grid Conditions

This article proposes a novel multiobjective control strategy (MOCS) for improving the performance of grid-tied photovoltaic (GTPV) system without phase-locked loop (PLL) operating under abnormal grid voltage conditions such as harmonically distorted voltages, grid faults, and weak grid. The proposed control strategy effectively mitigates the harmonics in grid injected current, eliminates the oscillations in grid injected power, and enhances the low-voltage ride-through (LVRT) capability simultaneously during abnormal grid conditions. The LVRT capability of the GTPV system is enhanced by incorporating an active power regulator (APR) with dynamic reactive power support (DRPS) controller in the proposed control strategy. The proposed MOCS is designed in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha \beta $ </tex-math></inline-formula> stationary reference frame. Furthermore, the proposed MOCS consists of an advanced phase compensated multiresonant (APCMR) current controller and a phase-shifted reference current generator (PSRCG) integrated with the APR with DRPS controller. The APCMR suppresses the grid current harmonics under weak and distorted grid voltages and the PSRCG eliminates active and reactive power oscillations at the grid side caused by grid faults. The synchronization mechanism of the GTPV system is included in the PSRCG, and therefore, the requirement of PLL is avoided. Consequently, the associated computational burden on the digital controller is reduced. The effectiveness of the proposed control strategy is validated using the OPAL-RT digital simulator and offers superior performance compared to the conventional control strategy (COCS).

Research on modeling and control strategy of lithium battery energy storage system in new energy consumption

Energy storage technology is one of the effective means to promote the consumption of new energy. It has the advantages of improving the flexibility and stability of power grid. Energy storage plays an important role in improving the peaking and valley filling function of the load side of the power grid. Based on the two-stage topology of the energy storage system, this paper establishes the mirror model of the practical application engineering of the energy storage system, and uses the data-driven method to establish the energy storage battery model. On this basis, the multi-objective control strategy is adopted for the peak regulating power of the energy storage system and the load state balance of the battery. The support vector machine algorithm is used to predict the daily load data of the power grid, and the constant power algorithm is proposed to control the battery control node signal. Finally, taking the battery compartment of the energy storage system as the simulation object, the effectiveness of the proposed control strategy is verified, which provides a theoretical basis for the topic research.

Multi-objective control of transient process of hydropower plant with two turbines sharing one penstock under combined operating conditions

This paper studies the multi-objective control of transient process of hydropower plant with two turbines sharing one penstock under combined operating conditions (COCs). Firstly, the model of transient process of hydropower plant is presented. The illustration of four COCs is provided. Then, the multi-objective control strategy of transient process under COCs is designed. The decision variables and objective functions are selected. The control scheme and decision method are determined. Finally, the most favourable superposition time and the most unfavourable superposition time under COCs are determined. The results indicate that the objective function contains three objectives, i.e. spiral case outlet pressures, draft tube inlet pressures and rotational speeds of turbines. The most favourable superposition time under COC-1 can realize that the objectives reach control requirements simultaneously. The most favourable superposition time under COC-2 and the most unfavourable superposition time under COC-1 and COC-2 cannot realize that the objectives reach control requirements simultaneously, but can balance the objectives to the maximum extent. It is unnecessary to distinguish the most favourable superposition time and the most unfavourable superposition time strictly under COC-3. There is no the concepts of the most favourable superposition time and the most unfavourable superposition time under COC-4.

ADMM-Based Multi-Objective Control Scheme for Mitigating the Impact of High Penetration DER Integration in the Modern Distribution Systems

The high penetration of renewable energy sources in modern distribution networks poses challenges for grid voltage regulation. In this study, a multi-agent distributed voltage control strategy based on the proximal Jacobian alternating direction method of multipliers (PJ-ADMM) is proposed for distribution power systems with a high penetration of photovoltaic (PV) resources coordinated with battery energy storage systems (BESS). In this context, all active and reactive power outputs of the PV are locally optimized through smart inverters to improve the grid voltage with minimum power loss. Uncertainties associated with the solar energy generation and load demands are considered in the defined scenarios. This study consists of two phases. In the first phase, the voltage control problem is formulated as an optimization problem to regulate the voltages within an acceptable limit with fast convergence. In the second phase, a coordinated voltage control strategy for smart PV inverters and BESS is proposed to allocate the power capacity of the battery energy storage systems and the active power loss reduction. Finally, the proposed method is tested on modified IEEE 13-bus, 33-bus and 141-bus distribution systems using MATLAB/Simulink and MATPOWER. A comparison of the results of the voltage profiles with and without the control algorithm demonstrated the efficacy, robustness, and scalability of the distributed scheme for voltage improvement and optimal utilization of PV power under different scenarios.

Control of ILC in an Autonomous AC–DC Hybrid Microgrid With Unbalanced Nonlinear AC Loads

This article presents a multiobjective control scheme for a bidirectional interlinking converter (ILC) of renewable energy based ac–dc hybrid microgrid. The prime focus of the power management algorithm is to feed the ac loads, and dc loads from ac source, and photovoltaic battery energy storage (BES), respectively. The second objective is to utilize the solar power to maximum capacity, by feeding it to dc load, BES, and ac load, in the given order of priority. The ac and dc subgrids operate independently, while supporting each other in case of overloading/underloading, thus reducing the amount of power reserve required in each, and eliminating the need of dump loads. The ANFIS-MN-based power quality control is integrated in order to ensure the total harmonics distortion of ac source current remains below 5% as per IEEE 519 standard, irrespective of the nature of load connected. In addition, the ILC control prevents any negative sequence currents from entering the ac source, even if there is any open circuit fault leading to unbalanced loading on the three phases. The performance of ANFIS-MN-based ILC control is demonstrated to be superior when compared with the conventional and contemporary techniques under different scenarios.

Cooperative Multi-Objective Control of Heterogeneous Vehicle Platoons on Highway with Varying Slopes

Stability, vehicle safety, energy saving, and passenger comfort are the major objectives of vehicle platooning control. These objectives are coupled, interrelated, and even conflicting, so integrated optimization of multiple objectives is quite challenging. Particularly for heterogeneous platoons, the difficulties are intensified for the differences in vehicle dynamics. In this paper, the concept of symmetry is utilized in the platooning control, that is, the design method of each vehicle’s controller is the same. For each controller, it is to solve the optimal solution of multi-objective collaborative optimization. The concept of asymmetry is meanwhile embodied in the parameter setting of each controller, for the vehicle heterogeneity. The contents of this study are as follows. First, a mathematical model is established, in which the differences in vehicle dynamic characteristics of heterogeneous platoon, road slope, and aerodynamics are all taken into account. Then, based on distributed nonlinear model predictive control (DNMPC) method, multi-objective control strategies are proposed for the leader and followers, cooperatively. Furthermore, a weight coefficient optimization method is presented, to further improve the platoon’s multi-objective synthesis performance. Finally, comparative experiments are carried out. Results demonstrate that, compared with the classic cruise control method of vehicle platoons, the proposed approach can reduce energy consumption by more than 5% and improve tracking performance on the premise of passenger comfort. Real-road experiments verify that the proposed control system can function effectively and satisfy the computational requirements in real applications.

Multi-Objective Control Strategy for Power Quality Improvement in Wind-Solar Distributed Generation System Under Harmonically Distorted Grid

Grid-interactive wind-solar distributed generation system (DGS) experiences substantial power quality (PQ) challenges pertaining to incessant voltage distortions in the utility grid and nonlinear loads at the point of common intersection. Such system non-idealities cause distortions in grid currents and infuse ripples in grid active and reactive powers. The effect of harmonically distorted voltages on DGS is further intensified in case of wind generation by means of a doubly-fed induction generator (DFIG), as its stator is directly coupled to the grid. Significant performance degradation with regard to distortions in DFIG currents, and ripples in stator power as well as DFIG torque, are observed. In this context, a control strategy, based on multiresonant higher order generalized integrator for harmonic estimation (MRHGI-HE) and a ϵ-normalized sign regressor least mean fourth (ϵ-NSRLMF) algorithm, for DGS employing solar photovoltaic array and DFIG-based wind generator, is presented in this article. Based on selected objective, the control approach puts-forth a feasible solution for alleviating the aforementioned PQ challenges. The MRHGI-HE provides improved harmonics estimation and superior disturbance rejection, than its conventional counterpart. Besides, ϵ-NSRLMF algorithm exhibits enhanced system dynamics, as compared with least mean fourth algorithm. The multiobjective control strategy is corroborated through extensive simulation and experimental studies, and results demonstrate the PQ improvements in the DGS.

Distributed Real-Time Multi-Objective Control of a Virtual Power Plant in DC Distribution Systems

Due to the strong coupling between power injections and bus voltages in DC distribution networks, it is impossible to achieve economic dispatch and recover the nominal voltages of all buses, simultaneously. In light of this, a multi-objective optimization-based control model is formulated for the multiple distributed energy resources (DERs) within the DC distribution network to be dispatched as a virtual power plant. A compromise can be made between the economic efficiency and voltage distribution quality by placing different weights on the multiple objectives. To this end, a distributed real-time multi-objective control strategy is proposed. Specifically, facilitated by sequential peer-to-peer communication, the dynamics of the cyber-physical DC distribution system imitate the primal-dual subgradient algorithm to the multi-objective optimization, thus can iteratively converge to its optimal solution. A dynamic weight adjustment mechanism is further designed to bound the bus voltages within a feasible range. This strategy is plug-and-play, privacy-preserving, robust to communication failures, and responsive to change of operating conditions compared to its centralized counterpart. Simulation studies of two typical DC distribution feeders verify the effectiveness of the proposed approach in various scenarios.

A Multi-Objective Current Compensation Strategy for Photovoltaic Grid-Connected Inverter

This paper aims at resolving problems of harmonic, reactive power and current imbalance. In the process of photovoltaic grid connection, a multi-objective control strategy is proposed, in which the three-phase network controller simultaneously controls the harmonic and realizes the reactive power and current balance. The [Formula: see text], [Formula: see text] current detection method, which is based on the current reactive power characteristic theory, can detect the compensation current. In order to reduce the measurement error of PLL, a software PLL based on decoupled double synchronous reference coordinate transformation is introduced, and the principle of multi-objective rule of three-stage network inverter is also given. The simulation and test results show that the harmonic distortion rate decreases from 2.57% to 1.10% with the proposed strategy, and the unbalance between reactive power and current is compensated, which verifies the correctness and feasibility of the strategy.

A multi-objective control scheme of a voltage source converter with battery–supercapacitor energy storage system used for power quality improvement

Conventionally, an energy storage system and two Voltage Source Converters (VSCs) are required to combine the operation of Distribution Static Compensator (DSTATCOM) and Uninterruptible Power Supply (UPS). The DSTATCOM’s VSC can compensate for the source voltage sag and swell. However, it cannot compensate for the voltage interruption. On the other hand, the VSC of the UPS can compensate for the source voltage interruption, but it cannot compensate for the voltage sag and swell. This paper proposes a control algorithm that uses a single VSC and a battery–supercapacitor energy storage system to function as DSTATCOM and UPS. The VSC and the battery–supercapacitor energy storage system mitigates the sag, swell, and interruption of the source voltages. Under the normal source voltages, this system mitigates the load current harmonics, unbalance, and reactive power. The proposed control algorithm uses a State of Charge (SoC) based scheme to control the battery–supercapacitor energy storage system. This control scheme protects the battery and supercapacitors from overcharging. Also, it eliminates the ripples in the load angle and increases the battery cycle life. The efficacy of the proposed control scheme has been verified through the experimental setup of the VSC with the battery–supercapacitor energy storage system in the laboratory.

A sliding mode control strategy for active horizontal seat suspension under realistic input vibration

In the paper, an innovative active control of a horizontal seat suspension system is discussed in context of realistic input vibration that occurs in the cabins of agricultural tractors. The vibration reduction system is represented by the state-space model and its numerical parameters are determined experimentally. For such a reliable mathematical representation of the system dynamics, the finite-time sliding mode controller is successfully designed for different spectral classes of the random excitation signal. A multi-objective control strategy is implemented in order to satisfy opposite requirements, that is, at the limited value of active force, a horizontal vibration of the seated human body is considerably reduced, but the suspension travel does not exceed unacceptable stroke of the system. Laboratory measurements of the seated human body have shown improved comfort of drivers under fore-and-aft vibrations.

Double Deep Reinforcement Learning-Based Energy Management for a Parallel Hybrid Electric Vehicle With Engine Start–Stop Strategy

Committed to optimizing the fuel economy of hybrid electric vehicles (HEVs), improving the working conditions of the engine, and promoting research on deep reinforcement learning (DRL) in the field of energy management strategies (EMSs), this article first proposed a DRL-based EMS combined with a rule-based engine start–stop strategy. Moreover, considering that both the engine and the transmission are controlled components, this article developed a novel double DRL (DDRL)-based EMS, which uses a deep Q-network (DQN) to learning the gear-shifting strategy and uses a deep deterministic policy gradient (DDPG) to control the engine throttle opening, and the DDRL-based EMS realizes the multiobjective synchronization control by different types of learning algorithms. After off-line training, the simulation result of the online test shows that the fuel consumption gaps of the proposed DRL- and DDRL-based EMSs are −0.55% and 2.33% compared to that of the deterministic dynamic programming (DDP)-based EMS by overcoming some inherent flaws of DDP, respectively. The computational efficiency has been significantly improved, and the average output time per action is 0.91 ms. Therefore, the control strategy that combines learning- and rule-based controls and the multiobjective control strategies both have the potential to ensure optimization and real-time efficiency.