Alireza Soroudi

Decision making under uncertainty in energy systems: State of the art

The energy system studies include a wide range of issues from short term (e.g. real-time, hourly, daily and weekly operating decisions) to long term horizons (e.g. planning or policy making). The decision making chain is fed by input parameters which are usually subject to uncertainties. The art of dealing with uncertainties has been developed in various directions and has recently become a focal point of interest. In this paper, a new standard classification of uncertainty modeling techniques for decision making process is proposed. These methods are introduced and compared along with demonstrating their strengths and weaknesses. The promising lines of future researches are explored in the shadow of a comprehensive overview of the past and present applications. The possibility of using the novel concept of Z-numbers is introduced for the first time.

Possibilistic Evaluation of Distributed Generations Impacts on Distribution Networks

In deregulated power systems, the distribution network operator (DNO) is not responsible for investment in distributed generation (DG) units, and they are just concerned about the best architecture ensuring a good service quality to their customers. The investment and operating decisions related to DG units are then taken by entities other than DNO which are exposed to uncertainty. The DNO should be able to evaluate the technical effects of these uncertain decisions. This paper proposes a fuzzy evaluation tool for analyzing the effect of investment and operation of DG units on active losses and the ability of distribution network in load supply at presence of uncertainties. The considered uncertainties are related to load values, installed capacity, and operating schedule of DG units. The proposed model is applied on a test system and also a real French urban network in order to demonstrate its functionality in evaluating the distribution expansion options.

Possibilistic-Scenario Model for DG Impact Assessment on Distribution Networks in an Uncertain Environment

The distribution network operators (DNOs) are responsible for securing a diverse and viable energy supply for their customers so the technical and economical impacts of distributed generation (DG) units are of great concerns. Traditionally, the DNOs try to maximize the technical performance of the distribution network, but it is evident that the first step in optimizing a quantity is being able to calculate it. The DG investment/operation which is performed by distributed generation operators/owners (DGOs) (under unbundling rules) has made this task more complicated. This is mainly because the DNO is faced with the uncertainties related to the decisions of DG investors/operators where some of them can be probabilistically modeled while the others are possibilistically treated. This paper proposes a hybrid possibilistic-probabilistic DG impact assessment tool which takes into account the uncertainties associated with investment and operation of renewable and conventional DG units on distribution networks. This tool would be useful for DNOs to deal with the uncertainties which some of them can be modeled probabilistically and some of them are described possibilistically. The proposed method has been tested on a test system and a large-scale real distribution network to demonstrate its strength and flexibility.

A Probabilistic Modeling of Photo Voltaic Modules and Wind Power Generation Impact on Distribution Networks

The rapid growth in use of renewable intermittent energy resources, like wind turbines (WTs) and solar panels, in distribution networks has increased the need for having an accurate and efficient method of handling the uncertainties associated with these technologies. In this paper, the unsymmetrical two point estimate method (US2PEM) is used to handle the uncertainties of renewable energy resources. The uncertainty of intermittent generation of WT, photo voltaic cells, and also electric loads, as input variables, are taken into account. The variation of active losses and imported power from the main grid are defined as output variables. The US2PEM is compared to symmetrical two point estimate method, Gram-Charlier method, and Latin hypercube sampling method, where Monte Carlo simulation is used as a basis for comparison. The validity of the proposed method is examined by applying it on a standard radial 9-node distribution network and a realistic 574-node distribution network.

IGDT Based Robust Decision Making Tool for DNOs in Load Procurement Under Severe Uncertainty

This paper presents the application of information gap decision theory (IGDT) to help the distribution network operators (DNOs) in choosing the supplying resources for meeting the demand of their customers. The three main energy resources are pool market, distributed generations (DGs), and the bilateral contracts. In deregulated environment, the DNO is faced with many uncertainties associated to the mentioned resources which may not have enough information about their nature and behaviors. In such cases, the classical methods like probabilistic methods or fuzzy methods are not applicable for uncertainty modeling because they need some information about the uncertainty behaviors like probability distribution function (PDF) or their membership functions. In this paper, a decision making framework is proposed based on IGDT model to solve this problem. The uncertain parameters considered here, are as follows: price of electricity in pool market and demand of each bus. The robust strategy of DNO is determined to hedge him against the risk of increasing the total cost beyond what it is willing to pay. The effectiveness of the proposed tool is assessed and demonstrated by applying it on a large distribution network.

Corrective Voltage Control Scheme Considering Demand Response and Stochastic Wind Power

This paper proposes a new approach for corrective voltage control (CVC) of power systems in presence of uncertain wind power generation and demand values. The CVC framework deals with the condition that a power system encounters voltage instability as a result of severe contingencies. The uncertainty of wind power generation and demand values is handled using a scenario-based modeling approach. One of the features of the proposed methodology is to consider participation of demand-side resources as an effective control facility that reduces control costs. Active and reactive redispatch of generating units and involuntary load curtailment are employed along with the voluntary demand-side participation (demand response) as control facilities in the proposed CVC approach. The CVC is formulated as a multi-objective optimization problem. The objectives are ensuring a desired loading margin while minimizing the corresponding control cost. This problem is solved using ε-constraint method, and fuzzy satisfying approach is employed to select the best solution from the Pareto optimal set. The proposed control framework is implemented on the IEEE 118-bus system to demonstrate its applicability and effectiveness.

Stochastic Multiperiod OPF Model of Power Systems With HVDC-Connected Intermittent Wind Power Generation

This paper presents a new model for a stochastic multiperiod optimal power-flow (SMP-OPF) problem which includes an offshore wind farm connected to the grid by a line-commutated converter high-voltage dc link. The offshore wind farm is composed of doubly fed induction generators (DFIGs), and the DFIGs capability curve is considered in order to obtain a more realistic dispatch for wind farms. The uncertainties of wind power generation are also taken into account using a scenario-based approach which can be adopted by the system operator to obtain the optimal active and reactive power schedules for thermal and wind power generation units. To illustrate the effectiveness of the proposed approach, it is applied on the IEEE 118-bus test system. The obtained results demonstrate the capability of the proposed SMP-OPF model for the determination of the optimal operation of power systems.

Optimal DR and ESS Scheduling for Distribution Losses Payments Minimization Under Electricity Price Uncertainty

The distribution network operator is usually responsible for increasing the efficiency and reliability of network operation. The target of active loss minimization is in line with efficiency improvement. However, this approach may not be the best way to decrease the losses payments in an unbundled market environment. This paper investigates the differences between loss minimization and loss payment minimization strategies. It proposes an effective approach for decreasing the losses payment considering the uncertainties of electricity prices in a day ahead energy market using energy storage systems and demand response. In order to quantify the benefits of the proposed method, the evaluation of the proposed technique is carried out by applying it on a 33-bus distribution network.

Energy Hub Management with Intermittent Wind Power

The optimal energy management in energy hubs has recently attracted a great deal of attention around the world. The energy hub consists of several inputs (energy resources) and outputs (energy consumptions) and also some energy conversion/storage devices. The energy hub can be a home, large consumer, power plant, etc. The objective is to minimize the energy procurement costs (fuel/electricity/environmental aspects) subject to a set of technical constraints. One of the popular options to be served as the input resource is renewable energy like wind or solar power. Using the renewable energy has various benefits such as low marginal costs and zero environmental pollution. On the other hand, the uncertainties associated with them make the operation of the energy hub a difficult and risky task. Besides, there are other resources of uncertainties such as the hourly electricity prices and demand values. Hence, it is important to determine an economic schedule for energy hubs, with an acceptable level of energy procurement risk. Thus, in this chapter a comprehensive multiobjective model is proposed to minimize both the energy procurement cost and risk level in energy hub. For controlling the pernicious effects of the uncertainties, conditional value at risk (CVaR) is used as risk management tool. The proposed model is formulated as a mixed integer nonlinear programming (MINLP) problem and solved using GAMS. Simulation results on an illustrative test system are carried out to demonstrate the applicability of the proposed method.

Information Gap Decision Theory-Based Congestion and Voltage Management in the Presence of Uncertain Wind Power

The supply of electrical energy is being increasingly sourced from renewable generation. The variability and uncertainty of renewable generation, compared to a dispatchable plant, is a significant dissimilarity of concern to the traditionally reliable and robust power system. This change is driving the power system toward a more flexible entity that carries greater amounts of reserve. For congestion management purposes, it is of benefit to know the probable and possible renewable generation dispatch, but to what extent will these variations effect the management of congestion on the system? Reactive power generation from wind generators and demand response flexibility are the decision variables here in a risk averse multiperiod AC optimal power flow (OPF) seeking to manage congestion on distribution systems. Information Gap Decision Theory is used to address the variability and uncertainty of renewable generation. In addition, this work considers the natural benefits to the congestion on a system from the over estimation of wind forecast; providing an opportunistic schedule for both demand response nodes and reactive power provision from distributed generation.