A review of co-optimization approaches for operational and planning problems in the energy sector

This paper contributes to a comprehensive perspective on the application of co-optimization in the energy sector – tracking the frontiers and trends in the field and identifying possible research gaps – based on a systematic literature review of 211 related studies. The use of co-optimization is addressed from a variety of perspectives by splitting the studies into ten key categories. Research has consistently shown that co-optimization approaches can be technically challenging and it is usually a data-intensive procedure. Overall, a set of techniques such as relaxation, decomposition and linear approaches have been proposed for reducing the inherent nonlinear model's complexities. The need to coordinate the necessary data from multiples actors might increase the complexity of the problem since security and confidentiality issues would also be put on the table. The evidence from our review seems to suggest a pertinent role for addressing real-case systems in future models instead of using theoretical test cases as considered by most studies. The identified challenges for future co-optimization models include (i) dealing with the treatment of uncertainties and (ii) take into account the trade-offs among modelling fidelity, spatial granularity and geographical coverage. Although there is also a growing body of literature that recognizes the importance of co-optimization focused on integrating supply and demand-side options, there has been little work in the development of co-optimization models for long-term decision-making, intending to recognize the impact of short-term variability of both demand and RES supply and well suited to systems with a high share of RES and under different demand flexibility conditions. The research results represent a further step towards the importance of developing more comprehensive approaches for integrating short-term constraints in future co-optimized planning models. The findings provide a solid evidence base for the multi-dimensionality of the co-optimization problems and contriThis work is supported by the National Council for Scientific and Technological Development (CNPq), Brazil. This work has been supported by FCT – Fundaça˜o para a Ciˆencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

Optimization models and algorithms for demand response in smart grid.

For demand response in smart grid, a utility company wants to minimize total electricity cost and end users want to maximize their own utility. The latter is considered to consist of two parts in this research: electricity cost and convenience/comfort. We first develop a system optimal (SO) model and a user equilibrium (UE) model for the utility company and end users, respectively and compare the difference of the two. We consider users\u27 possible preference on convenience over cost-saving under the real-time pricing in smart grid, and each user is assumed to have a preferred time window for using a particular appliance. As a result, each user in the proposed energy consumption game wishes to maximize a payoff or utility consisting of two parts: the negative of electricity cost and the convenience of using appliances during their preferred time windows. Numerical results show that users with less flexibility on their preferred usage times have larger impact on the system performance at equilibrium. Second, we found that instead of minimizing total cost, if utility company is regulated to maximize the social welfare, the user equilibrium model can achieve identical optimal solution as the system optimal model. We then design a demand response pricing frame work to accomplish this goal under alternative secondary objectives. We also investigate the non-uniqueness of the user equilibrium solution and prove that there exist alternative user equilibrium solutions. In this case, robust pricing is considered using multi-level optimization for the user equilibrium. Third, we study empirical data from a demand response pilot program in Kentucky in an attempt to understand consumer behavior under demand response and to characterize the thermo dynamics when set point for heat, ventilation and air conditioning (HVAC) is adjusted for demand response. Although sample size is limited, it helps to reveal the great variability in consumers\u27 response to demand response event. Using the real data collected, we consider to minimize the peak demand for a system consisting of smart thermostats, advanced hot water heaters and battery systems for storage. We propose a mixed integer program model as well as a heuristic algorithm for an optimal consumption schedule so that the system peak during a designated period is minimized. Therefore, we propose a consumption scheduling model to optimally control these loads and storage in maximizing efficiency without impacting thermal comfort. The model allows pre-cooling and pre-heating of homes to be performed for variable loads in low-demand times. We propose several future works. First, we introduce the concept of elastic demand to our SO model and UE model. The system problem maximizes net benefit to the energy consumers and the user problem is the usual one of finding equilibrium with elastic demand. The Karush-Kuhn-Tucker (KKT) conditions can be applied to solve the elastic demand problems. We also propose to develop algorithms for multi-level pricing models and further collect and analyze more field data in order to better understand energy users\u27 consumption behavior

Bi-Level Optimization Considering Uncertainties of Wind Power and Demand Response

Recently, world-wide power systems have been undergone a paradigm change with increasing penetration of renewable energy. The renewable energy is clean with low operation cost while subject to significant variability and uncertainty. Therefore, integration of renewables presents various challenges in power systems. Meanwhile, to offset the uncertainty from renewables, demand response (DR) has gained considerable research interests because of DR’s flexibility to mitigate the uncertainty from renewables. In this dissertation, various power system problems using bi-level optimization are investigated considering the uncertainties from wind power and demand response. In power system planning, reactive power planning (RPP) under high-penetration wind power is studied in this dissertation. To properly model wind power uncertainty, a multi-scenario framework based on alternating current optimal power flow (ACOPF) considering the voltage stability constraint under the worst wind scenario and transmission N-1 contingency is developed. The objective of RPP in this work is to minimize the VAR investment and the expected generation cost. Benders decomposition is used to solve this model with an upper level problem for VAR allocation optimization and generation cost minimization as a lower problem. Then, several problems related wind power and demand response uncertainties under power market operation are investigated. These include: an efficient and effective method to calculate the LMP intervals under wind uncertainty is proposed; the load serving entities’ strategic bidding through a coupon-based demand response (CBDR) with which a load serving entity (LSE) may participate in the electricity market as strategic bidders by offering CBDR programs to customers; the impact of financial transmission right (FTR) with CBDR programs is also studied from the perspective of LSEs; and the stragegic scheduling of energy storages owned by LSEs considering the impact of charging and discharging on the bus LMP. In these problems, a bi-level optimization framework is presented with various objective functions representing different problems as the upper level problems and the ISO’s economic dispatch (ED) as the lower level problem. The bi-level model is addressed with mathematic program with equilibrium constraints (MPEC) model and mixed-integer linear programming (MILP), which can be easily solved with the available optimization software tool

Reserve services provision by demand side resources in systems with high renewables penetration using stochastic optimization

It is widely recognized that renewable energy sources are likely to represent a significant portion of the production mix in many power systems around the world, a trend expected to be increasingly followed in the coming years due to environmental and economic reasons. Among the different endogenous renewable sources that may be used in order to achieve reductions in the carbon footprint related to the electricity sector and increase the economic efficiency of the generation mix, wind power generation has been one of the most popular options. However, despite the potential benefits that arise from the integration of these resources in the power system, their large-scale integration leads to additional problems due to the fact that their production is highly volatile. As a result, apart from the typical sources of uncertainty that the System Operators have to face, such as system contingencies and intra-hour load deviations, through the deployment of sufficient levels of reserve generation, additional reserves must be kept in order to maintain the balance between the generation and the consumption. Furthermore, a series of other problems arise, such as efficiency loss because of ramping of conventional units, environmental costs because of increased emissions due to suboptimal unit commitment and dispatch and more costly system operation and maintenance. Recently, it has been recognized that apart from the generation side, several types of loads may be deployed in order to provide system services and especially, different types of reserves, through demand response. The contribution of demand side reserves to accommodate higher levels of wind power generation penetration is likely to be of substantial importance in the future and therefore, the integration of these resources in the system operations needs to be thoroughly studied. This thesis deals with the aspects of demand response as regards the integration of wind power generation in the power system. First, a mapping of the current status of demand response internationally is attempted, followed also by a discussion concerning the opportunities, the benefits and the barriers to the widespread adoption of demand side resources. Then, several joint energy and reserve market structures are developed which explicitly incorporate demand side resources that may contribute to energy and reserve services. Two-stage stochastic programming is employed in order to capture the uncertainty of wind power generation. Moreover, several aspects of demand response are considered such as the capability of providing contingency and load following reserves, the appropriate modeling of industrial consumer processes load and the load recovery effect. Finally, this thesis investigates the effect of demand side resources on the risk that is associated with the decisions of the System Operator through appropriate risk management techniques, proposing also a novel methodology of handling risk as an alternative to the commonly used technique.It is widely recognized that renewable energy sources are likely to represent a significant portion of the production mix in many power systems around the world, a trend expected to be increasingly followed in the coming years due to environmental and economic reasons. Among the different endogenous renewable sources that may be used in order to achieve reductions in the carbon footprint related to the electricity sector and increase the economic efficiency of the generation mix, wind power generation has been one of the most popular options. However, despite the potential benefits that arise from the integration of these resources in the power system, their large-scale integration leads to additional problems due to the fact that their production is highly volatile. As a result, apart from the typical sources of uncertainty that the System Operators have to face, such as system contingencies and intra-hour load deviations, through the deployment of sufficient levels of reserve generation, additional reserves must be kept in order to maintain the balance between the generation and the consumption. Furthermore, a series of other problems arise, such as efficiency loss because of ramping of conventional units, environmental costs because of increased emissions due to suboptimal unit commitment and dispatch and more costly system operation and maintenance. Recently, it has been recognized that apart from the generation side, several types of loads may be deployed in order to provide system services and especially, different types of reserves, through demand response. The contribution of demand side reserves to accommodate higher levels of wind power generation penetration is likely to be of substantial importance in the future and therefore, the integration of these resources in the system operations needs to be thoroughly studied. This thesis deals with the aspects of demand response as regards the integration of wind power generation in the power system. First, a mapping of the current status of demand response internationally is attempted, followed also by a discussion concerning the opportunities, the benefits and the barriers to the widespread adoption of demand side resources. Then, several joint energy and reserve market structures are developed which explicitly incorporate demand side resources that may contribute to energy and reserve services. Two-stage stochastic programming is employed in order to capture the uncertainty of wind power generation. Moreover, several aspects of demand response are considered such as the capability of providing contingency and load following reserves, the appropriate modeling of industrial consumer processes load and the load recovery effect. Finally, this thesis investigates the effect of demand side resources on the risk that is associated with the decisions of the System Operator through appropriate risk management techniques, proposing also a novel methodology of handling risk as an alternative to the commonly used technique

Demand response performance and uncertainty: A systematic literature review

The present review has been carried out, resorting to the PRISMA methodology, analyzing 218 published articles. A comprehensive analysis has been conducted regarding the consumer's role in the energy market. Moreover, the methods used to address demand response uncertainty and the strategies used to enhance performance and motivate participation have been reviewed. The authors find that participants will be willing to change their consumption pattern and behavior given that they have a complete awareness of the market environment, seeking the optimal decision. The authors also find that a contextual solution, giving the right signals according to the different behaviors and to the different types of participants in the DR event, can improve the performance of consumers' participation, providing a reliable response. DR is a mean of demand-side management, so both these concepts are addressed in the present paper. Finally, the pathways for future research are discussed.This article is a result of the project RETINA (NORTE-01-0145- FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). We also acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team, and grants CEECIND/02887/2017 and SFRH/BD/144200/2019.info:eu-repo/semantics/publishedVersio

Smart home energy management: An analysis of a novel dynamic pricing and demand response aware control algorithm for households with distributed renewable energy generation and storage

Home energy management systems (HEMS) technology can provide a smart and efficient way of optimising energy usage in residential buildings. One of the main goals of the Smart Grid is to achieve Demand Response (DR) by increasing end users’ participation in decision making and increasing the level of awareness that will lead them to manage their energy consumption in an efficient way. This research presents an intelligent HEMS algorithm that manages and controls a range of household appliances with different demand response (DR) limits in an automated way without requiring consumer intervention. In addition, a novel Multiple Users and Load Priority (MULP) scheme is proposed to organise and schedule the list of load priorities in advance for multiple users sharing a house and its appliances. This algorithm focuses on control strategies for controllable loads including air-conditioners, dishwashers, clothes dryers, water heaters, pool pumps and electrical vehicles. Moreover, to investigate the impact on efficiency and reliability of the proposed HEMS algorithm, small-scale renewable energy generation facilities and energy storage systems (ESSs), including batteries and electric vehicles have been incorporated. To achieve this goal, different mathematical optimisation approaches such as linear programming, heuristic methods and genetic algorithms have been applied for optimising the schedule of residential loads using different demand side management and demand response programs as well as optimising the size of a grid connected renewable energy system. Thorough incorporation of a single objective optimisation problem under different system constraints, the proposed algorithm not only reduces the residential energy usage and utility bills, but also determines an optimal scheduling for appliances to minimise any impacts on the level of consumer comfort. To verify the efficiency and robustness of the proposed algorithm a number of simulations were performed under different scenarios. The simulations for load scheduling were carried out over 24 hour periods based on real-time and day ahead electricity prices. The results obtained showed that the proposed MULP scheme resulted in a noticeable decrease in the electricity bill when compared to the other scenarios with no automated scheduling and when a renewable energy system and ESS are not incorporated. Additionally, further simulation results showed that widespread deployment of small scale fixed energy storage and electric vehicle battery storage alongside an intelligent HEMS could enable additional reductions in peak energy usage, and household energy cost. Furthermore, the results also showed that incorporating an optimally designed grid-connected renewable energy system into the proposed HEMS algorithm could significantly reduce household electricity bills, maintain comfort levels, and reduce the environmental footprint. The results of this research are considered to be of great significance as the proposed HEMS approach may help reduce the cost of integrating renewable energy resources into the national grid, which will be reflected in more users adopting these technologies. This in turn will lead to a reduction in the dependence on traditional energy resources that can have negative impacts on the environment. In particular, if a significant proportion of households in a region were to implement the proposed HEMS with the incorporation of small scale storage, then the overall peak demand could be significantly reduced providing great benefits to the grid operator as well as the households