Memristive elements, known as memristors, memcapacitors and meminductors, have become an important topic of research in the electronics world in recent years. As there is still no efficient way to manufacture two-terminal memristive elements, many researchers have focused their efforts on designing emulator circuits that mimic these devices. In this study, a memcapacitor emulator circuit using Current Derivative Buffered Amplifier (CDBA) is proposed, which has significant advantages such as wide dynamic range, differential structure at the input port, high sloping rate and wide bandwidth. The main advantages of the emulator are that it is floating without grounding constraint, it is electronically adjustable, it has charge-controlled incremental and decremental modes and it has a simpler circuit structure since it does not contain a memristor. To ensure the integrity of the circuit theory, the results of the mathematical model and the simulation of the memcapacitor are given together. In addition, the characteristics of the experimentally investigated memcapacitor emulator are in good agreement with the simulation results. To provide an illustration of the performance of the proposed emulator, firstly the second-order active low-pass filter circuit and subsequently the amoeba learning circuit are selected as the working environment. The results show that the filtering performance of the proposed emulator at a value after the cut-off frequency in the filter circuit is $25\%$ more efficient than a standard capacitor and in terms of power consumption, it consumes $27.93\%$ less power than a standard capacitor. Moreover, the emulator successfully accomplishes the learning and data storage tasks in the amoeba learning circuit.

In this study, a novel modified Chua-like hyperchaotic circuit using the memcapacitor is presented. Since commercial memcapacitor production is currently unavailable, the previously designed equivalent circuit of the memcapacitor will be used in the chaotic circuit. The dynamic equations of the circuit are obtained, and therefore the effect of the memcapacitor is incorporated into the modified Chua circuit. The incorporation of both incremental and decremental configurations of the memcapacitor equivalent circuit into the chaotic system has enabled the successful realization of two distinct sets of chaotic attractors, demonstrating the versatility of the proposed design. Both experimental and simulation studies demonstrate that the newly designed hyperchaotic circuit driven by the memcapacitor generates the hyperchaotic complex oscillations and that the chaotic attractor is highly sensitive to the parameters and initial conditions. Dynamic analyses of the circuit such as Lyapunov exponents, bifurcation analyses, coexisting attractors, Fast Fourier Transform (FFT) spectra, complexity evaluation, and noise robustness analysis have also been successfully carried out. Additionally, the applicability of the proposed circuit in the field of secure communication has been verified through a chaos-based image encryption and decryption study, demonstrating strong performance in NPCR, UACI, and Shannon entropy tests. Compared to previously reported mem-element-based chaotic circuits, the proposed system distinguishes itself by exhibiting hyperchaotic behavior with coexisting attractors and by being verified through analog experimental implementation, demonstrating both its dynamical richness and practical hardware feasibility. The obtained results indicate that the proposed circuit possesses strong potential for use across a broad range of chaotic system applications.

Renewable energy sources (RESs) enable sustainable and environmentally friendly electricity generation. However, their intermittent nature makes it difficult to maintain energy balance. Smart grids (SGs) address this challenge by enabling grid control under variable demand and fluctuating generation. With the growing share of distributed generation, dynamic energy pricing has become increasingly important for sustaining the supply–demand balance in SGs. This study aims to regulate the interaction between variable demand and distributed generation in SGs using control strategies. The dynamic pricing framework was analyzed using closed-loop Proportional–Integral–Derivative (PID), Fractional-Order PID (FOPID)- and Adaptive Neuro Fuzzy Inference System (ANFIS)-based FOPID controllers. PID and FOPID parameters were tuned by pole placement with reference model matching, while the FOPID parameters in the ANFIS-FOPID structure were adaptively optimized using ANFIS. Energy supply–demand models were developed in MATLAB/Simulink, and the effects of each controller on system dynamics and energy prices were comparatively examined. The results indicate that ANFIS-FOPID achieves lower overshoot, shorter settling time, and more stable balancing performance, owing to its fractional-order flexibility and optimized parameters. In the model established in the MATLAB/Simulink environment, the controllers were evaluated based on integral of squared error (ISE), time-weighted integral absolute error (ITAE), root mean square error (RMSE), average unit energy price, price volatility, and coefficient of variation. A virtual energy storage model was added to the system. Disturbance and load change scenarios were also examined. The results showed that the ANFIS-FOPID controller provided the most balanced performance in terms of error reduction, suppression of price fluctuations, and reduction of the average unit energy price.