Optimal Scheduling of Electric Vehicle Charging with Deep Reinforcement Learning considering End Users Flexibility

The rapid growth of decentralized energy resources and especially Electric Vehicles (EV), that are expected to increase sharply over the next decade, will put further stress on existing power distribution networks, increasing the need for higher system reliability and flexibility. In an attempt to avoid unnecessary network investments and to increase the controllability over distribution networks, network operators develop demand response (DR) programs that incentivize end users to shift their consumption in return for financial or other benefits. Artificial intelligence (AI) methods are in the research forefront for residential load scheduling applications, mainly due to their high accuracy, high computational speed and lower dependence on the physical characteristics of the models under development. The aim of this work is to identify households' EV cost-reducing charging policy under a Time-of-Use tariff scheme, with the use of Deep Reinforcement Learning, and more specifically Deep Q-Networks (DQN). A novel end users flexibility potential reward is inferred from historical data analysis, where households with solar power generation have been used to train and test the designed algorithm. The suggested DQN EV charging policy can lead to more than 20% of savings in end users electricity bills

Review of Computational Intelligence Methods for Local Energy Markets at the Power Distribution Level to Facilitate the Integration of Distributed Energy Resources: State-of-the-art and Future Research

The massive integration of distributed energy resources in power distribution systems in combination with the active network management that is implemented thanks to innovative information and communication technologies has created the smart distribution systems of the new era. This new environment introduces challenges for the optimal operation of the smart distribution network. Local energy markets at power distribution level are highly investigated in recent years. The aim of local energy markets is to optimize the objectives of market participants, e.g., to minimize the network operation cost for the distribution network operator, to maximize the profit of the private distributed energy resources, and to minimize the electricity cost for the consumers. Several models and methods have been suggested for the design and optimal operation of local energy markets. This paper introduces an overview of the state-of-the-art computational intelligence methods applied to the optimal operation of local energy markets, classifying and analyzing current and future research directions in this area

Technical challenges associated with the integration of wind power into power systems

Wind power is going through a very rapid development. It is among the fastest growing power sources in the world, the technology is being developed rapidly and wind power is supplying significant shares of the energy in large regions. The integration of wind power in the power system is now an issue in order to optimize the utilization of the resource and to continue the high rate of installation of wind generating capacity, which is necessary so as to achieve the goals of sustainability and security of supply. This paper presents the main technical challenges that are associated with the integration of wind power into power systems. These challenges include effects of wind power on the power system, the power system operating cost, power quality, power imbalances, power system dynamics, and impacts on transmission planning. The main conclusion is that wind power's impacts on system operating costs are small at low wind penetrations (about 5% or less). At higher wind penetrations, the impact will be higher, although current results suggest the impact remains moderate with penetrations approaching 20%. In addition, the paper presents the technology and expectations of wind forecasting as well as cases where wind power curtailment could arise. Future research directions for a better understanding of the factors influencing the increased integration of wind power into power systems are also provided.Wind power Power generation Power systems Grid integration

Review of Deterministic and Probabilistic Wind Power Forecasting: Models, Methods, and Future Research

The need to turn to more environmentally friendly sources of energy has led energy systems to focus on renewable sources of energy. Wind power has been a widely used source of green energy. However, the wind’s stochastic and unpredictable behavior has created several challenges to the operation and stability of energy systems. Forecasting models have been developed and excessively used in recent decades in order to deal with these challenges. Deterministic forecasting models have been the main focus of researchers and are still being developed in order to improve their accuracy. Furthermore, in recent years, in order to observe and study the uncertainty of forecasts, probabilistic forecasting models have been developed in order to give a wider view of the possible prediction outcomes. Advanced probabilistic and deterministic forecasting models could be used in order to facilitate the energy systems operation and energy markets management. This paper introduces an overview of state-of-the-art wind power deterministic and probabilistic models, developing a comparative evaluation between the different models reviewed, identifying their advantages and disadvantages, classifying and analyzing current and future research directions in this area

Unified power flow controllers in smart power systems: models, methods, and future research

Power flow control has become increasingly important in recent years in the area of smart power systems that have to integrate increased shares of variable renewable energy sources. The unified power flow controller (UPFC) provides in real-time, simultaneously or selectively, active and reactive power flow control as well as voltage control in smart power systems. Several models and methods have been suggested for the control, analysis, operation, and planning of UPFCs in smart power systems. This study introduces a review of the state-of-the-art models and methods of UPFCs in smart power systems, analysing and classifying current and future research trends in this field

Particle Swarm Optimization in Residential Demand-Side Management: A Review on Scheduling and Control Algorithms for Demand Response Provision

Power distribution networks at the distribution level are becoming more complex in their behavior and more heavily stressed due to the growth of decentralized energy sources. Demand response (DR) programs can increase the level of flexibility on the demand side by discriminating the consumption patterns of end-users from their typical profiles in response to market signals. The exploitation of artificial intelligence (AI) methods in demand response applications has attracted increasing interest in recent years. Particle swarm optimization (PSO) is a computational intelligence (CI) method that belongs to the field of AI and is widely used for resource scheduling, mainly due to its relatively low complexity and computational requirements and its ability to identify near-optimal solutions in a reasonable timeframe. The aim of this work is to evaluate different PSO methods in the scheduling and control of different residential energy resources, such as smart appliances, electric vehicles (EVs), heating/cooling devices, and energy storage. This review contributes to a more holistic understanding of residential demand-side management when considering various methods, models, and applications. This work also aims to identify future research areas and possible solutions so that PSO can be widely deployed for scheduling and control of distributed energy resources in real-life DR applications