“An Integer Programming Power Optimization in Storage Systems”

AbstractThis paper presents a linear integer programming framework for effective power management in storage systems. A sample memory system with different data banks is considered for optimal energy consumption during data operations by manipulating the data among banks. The memory bank four-level power state schemes, namely, active, stand-by, nap, and power down states, are included for superior power management of the storage system by formulating a linear integer optimization framework that includes plausible data manipulations, energy consumption levels, data migration, and compression options. The numerical results illustrate the efficiency of the proposed framework in terms of power management of storage systems with respect to available approaches with two-level power state operations

Optimization Models for islanded micro-grids: A comparative analysis between linear programming and mixed integer programming

This paper presents a comparison of optimization methods applied to islanded micro-grids including renewable energy sources, diesel generators and battery energy storage systems. In particular, a comparative analysis between an optimization model based on linear programming and a model based on mixed integer programming has been carried out. The general formulation of these models has been presented and applied to a real case study micro-grid installed in Somalia. The case study is an islanded micro-grid supplying the city of Garowe by means of a hybrid power plant, consisting of diesel generators, photovoltaic systems and batteries. In both models the optimization is based on load demand and renewable energy production forecast. The optimized control of the battery state of charge, of the spinning reserve and diesel generators allows harvesting as much renewable power as possible or to minimize the use of fossil fuels in energy production

Optimizing the flash-RAM energy trade-off in deeply embedded systems

Deeply embedded systems often have the tightest constraints on energy consumption, requiring that they consume tiny amounts of current and run on batteries for years. However, they typically execute code directly from flash, instead of the more energy efficient RAM. We implement a novel compiler optimization that exploits the relative efficiency of RAM by statically moving carefully selected basic blocks from flash to RAM. Our technique uses integer linear programming, with an energy cost model to select a good set of basic blocks to place into RAM, without impacting stack or data storage. We evaluate our optimization on a common ARM microcontroller and succeed in reducing the average power consumption by up to 41% and reducing energy consumption by up to 22%, while increasing execution time. A case study is presented, where an application executes code then sleeps for a period of time. For this example we show that our optimization could allow the application to run on battery for up to 32% longer. We also show that for this scenario the total application energy can be reduced, even if the optimization increases the execution time of the code

Wind-CSP short-term coordination by MILP approach

This paper is on the maximization of total profit in a day-ahead market for a price-taker producer needing a short-term scheduling for wind power plants coordination with concentrated solar power plants, having thermal energy storage systems. The optimization approach proposed for the maximization of profit is a mixed-integer linear programming problem. The approach considers not only transmission grid constraints, but also technical operating constraints on both wind and concentrated solar power plants. Then, an improved short-term scheduling coordination is provided due to the more accurate modelling presented in this paper. Computer simulation results based on data for the Iberian wind and concentrated solar power plants illustrate the coordination benefits and show the effectiveness of the approach

Energy Management Systems of Microgrids

The distributed operation of parts of the system, denoted as microgrids or, more generally, as local energy communities, could be an effective answer to the issues posed by the increasing complexity of the modern power distribution systems facing the increasing penetration of renewable energy sources and the electrification of urban transportation. The results of the research activities described in the thesis can be divided into three main parts. The first one is the modeling and analysis of low voltage power distribution networks feeding residential, commercial and small-scale industrial consumers including distributed generation units and storage systems. It focuses on an optimization model that has been applied to the energy management system of an experimental microgrid. A mixed integer linear programming model is developed and presented, which takes into account the unbalanced operation of the LV network. The second part focuses on the day-ahead operational planning of a local energy community, which is assumed able to implement transactive energy control actions with allocation of the network power loss. The problem has been addressed by means of two different optimization procedures, namely a centralized mathematical programming model and a specific distributed optimization procedure based on the adoption of the alternating direction method of multipliers (ADMM). The third part is the day-ahead optimization of the operation of a local energy system consisting of photovoltaic units, energy storage systems and loads aimed at minimizing the electricity procurement cost, considering the uncertainties in the load and generation forecasts. Two mixed integer linear programming models are adopted, each for a different representation of the battery: a simple energy balance constraint and the Kinetic Battery Model. The chapter describes the generation of the scenarios, the construction of the scenario tree and the intraday decision-making procedure based on the solution of the multistage stochastic programming

Compressed Air Energy Storage-Part II: Application to Power System Unit Commitment

Unit commitment (UC) is one of the most important power system operation problems. To integrate higher penetration of wind power into power systems, more compressed air energy storage (CAES) plants are being built. Existing cavern models for the CAES used in power system optimization problems are not accurate, which may lead to infeasible solutions, e.g., the air pressure in the cavern is outside its operating range. In this regard, an accurate CAES model is proposed for the UC problem based on the accurate bi-linear cavern model proposed in the first paper of this two-part series. The minimum switch time between the charging and discharging processes of CAES is considered. The whole model, i.e., the UC model with an accurate CAES model, is a large-scale mixed integer bi-linear programming problem. To reduce the complexity of the whole model, three strategies are proposed to reduce the number of bi-linear terms without sacrificing accuracy. McCormick relaxation and piecewise linearization are then used to linearize the whole model. To decrease the solution time, a method to obtain an initial solution of the linearized model is proposed. A modified RTS-79 system is used to verify the effectiveness of the whole model and the solution methodology.Comment: 8 page

Flexibility Services to Minimize the Electricity Production from Fossil Fuels. A Case Study in a Mediterranean Small Island

The design of multi-carrier energy systems (MES) has become increasingly important in the last decades, due to the need to move towards more efficient, flexible and reliable power systems. In a MES, electricity, heating, cooling, water and other resources interact at various levels, in order to get optimized operation. The aim of this study is to identify the optimal combination of components, their optimal sizes and operating schedule allowing minimizing the annual cost for meeting the energy demand of Pantelleria, a Mediterranean island. Starting from the existing energy system (comprising diesel generators, desalination plant, freshwater storage, heat pumps and domestic hot water storages) the installation of solar resources (photovoltaic and solar thermal) and electrical storage were considered. In this way, the optimal scheduling of storage units injections, water desalination operation and domestic hot water production was deduced. An energy hub model was implemented using MATLAB to represent the problem. All equations in the model are linear functions, and variables are real or integer. Thus, a mixed integer linear programming algorithm was used for the solution of the optimization problem. Results prove that the method allows a strong reduction of operating costs of diesel generators also in the existing configuration

Coordinated operation of gas and electricity systems for flexibility study

The increase interdependencies between electricity and gas systems, driven by gas-fired power plants and gas electricity-driven compressors, necessitates detailed investigation of such interdependencies, especially in the context of increased share of renewable energy sources. 6 In this paper, the value of an integrated approach for operating gas and electricity systems is assessed. An outer approximation with equality relaxation (OA/ER) method is used to deal with the optimization class of mixed integer non-linear problem of integrated operation of gas and electricity systems. This method significantly improved the efficiency of the solution algorithm and achieved nearly 40% reduction in computation time compared to successive linear programming. The value of flexibility technologies including flexible gas compressors, demand side response, battery storage, and power-to-gas is quantified in the operation of integrated gas and electricity systems in GB 2030 energy scenarios for different renewable generation penetration levels. The modeling demonstrates that the flexibility options will enable significant cost savings in the annual operational costs of gas and electricity systems (up to 21%). On the other hand, the analysis carried out indicates that deployment of flexibility technologies support appropriately the interaction between gas and electricity systems