In this paper, we propose a new framework for the optimal virtual power plant (VPP) energy management problem considering correlated demand response (CDR). Our objective is to minimize the VPP operating cost while maintaining the power quality of the system. We formulate a risk-constrained two-stage stochastic program to address uncertainties in day-ahead and real-time electricity prices, renewable energy source’s (RES) generation processes, and the CDR relationship. The VPP can also maintain cooling and heating balances by coordinating combined cooling, heating, and power production (CCHP) and CDR units. Extensive simulation results show that the VPP can minimize the operating cost and ensure the energy balance and power quality by coordinating components in the framework we propose.

One of the most important sources of clean energy in the future is expected to be solar energy which is considered a real time source. Research efforts to optimize solar energy utilization are mainly concentrated on the components of solar energy systems. Photovoltaic (PV) modules are considered the main components of solar energy systems and PVs’ operations typically occur without any supervisory mechanisms, which means many external and/or internal obstacles can occur and hinder a system’s efficiency. To avoid these problems, the paper presents a system to address and detect the faults in a PV system by providing a safer and more time efficient inspection system in real time. In this paper, we proposing a real time inspection and fault detection system for PV modules. The system has two cameras, a thermal and a Charge-Coupled Device CCD. They are mounted on a drone and they used to capture the scene of the PV modules simultaneously while the drone is flying over the solar garden. A mobile PV system has been constructed primarily to validate our real time proposed system and for the proposed method in the Digital Image and Signal Processing Laboratory (DISPLAY) at Western Michigan University (WMU). Defects have been detected accurately in the PV modules using the proposed real time system. As a result, the proposed drone mounted system is capable of analyzing thermal and CCD videos in order to detect different faults in PV systems, and give location information in terms of panel location by longitude and latitude.

Due to the rapidly-changing technologies in the power industry, many new references addressing the frameworks and business models of the next-generation retail electricity market are entering the research community. In particular, considering new customers with considerable demand response awareness and so-called prosumers with localized power generation based on distributed energy resources (DERs), the next-generation retail electricity market infrastructure will be a level playing field for local energy transactions, strategic pricing scheme design, new business model design and building an innovative energy ecosystem. Consequently, there is an urgent need to keep track of international experiences and activities taking place in the field of the market mechanism design problem at the distribution level. This paper provides a comprehensive survey of recent technology developments and aims to inspire awareness of the further deregulation of the electricity market, especially in areas close to customers. We mainly bring attention to the more than 90 articles published during the past five years. The collected literature has been divided into different sections to discuss different aspects of the next-generation retail electricity market under the deregulated power industry.

A compact, cylindrical dielectric resonator antenna (CDRA), using radio frequency signals to identify different liquids is proposed in this paper. The proposed CDRA sensor is excited by a rectangular slot through a 3-mm-wide microstrip line. The rectangular slot has been used to excite the CDRA for $HE{M}_{11}$ mode at 5.25 GHz. Circuit model values (capacitance, inductance, resistance and transformer ratios) of the proposed CDRA are derived to show the true behaviour of the system. The proposed CDRA acts as a sensor due to the fact that different liquids have different dielectric permittivities and, hence, will be having different resonance frequencies. Two different types of CDRA sensors are designed and experimentally validated with four different liquids (Isopropyl, ethanol, methanol and water).

As the unconstrained integration of distributed photovoltaic (PV) power into a power grid will cause changes in the power flow of the distribution network, voltage deviation, voltage fluctuation, and so on, system operators focus on how to determine and improve the integration capacity of PV power rationally. By giving full consideration to the static security index constraints and voltage fluctuation, this paper proposes a maximum integration capacity optimization model of the PV power, according to different power factors for the PV power. Moreover, the proposed research analyzes the large-scale PV grid access capacity, PV access point, and multi-PV power plant output, by probability density distribution, sensitivity analysis, standard deviation analysis, and over-limit probability analysis. Furthermore, this paper establishes accessible capacity maximization problems from the Institute of Electrical and Electronics Engineers (IEEE) standard node system and power system analysis theory for PV power sources with constraints of voltage fluctuations. A MATLAB R2017B simulator is used for the performance analysis and evaluation of the proposed work. Through the simulation of the IEEE 33-node system, the integration capacity range of the PV power is analyzed, and the maximum integration capacity of the PV power at each node is calculated, providing a rational decision-making scheme for the planning of integrating the distributed PV power into a small-scale power grid. The results indicate that the fluctuations and limit violation probabilities of the power system voltage and load flow increase with the addition of the PV capacity. Moreover, the power loss and PV penetration level are influenced by grid-connected spots, and the impact of PV on the load flow is directional.

The optimal power flow problem over direct current (DC) grids is to minimize the cost of active power generation considering all power flow equations, the maximum capacitance of the lines, and voltage limited boundaries of all the buses. It is one of the famous non-convex problems in the power system area. In this paper, a new distributed consensus-based algorithm is proposed for solving optimal power flow for DC distribution systems. As this algorithm is based on semi-definite relaxation (SDR), a mathematical proof is provided to show the exactness of the SDR relaxation technique. Then, it is demonstrated that the proposed distributed method converges to the global optimal point while satisfying all system constraints. A detailed analysis of the proposed algorithm is provided by applying it to a modified version of the IEEE-30 bus system. Finally, this method is applied to different DC power flow case studies, such as 14, 30, 57, 118, 200-bus systems, to provide a strong performance assessment. The simulation results, when compared with the benchmark, are quite promising.